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Laurence DW, Wang S, Xiao R, Qian J, Mir A, Burkhart HM, Holzapfel GA, Lee CH. An investigation of how specimen dimensions affect biaxial mechanical characterizations with CellScale BioTester and constitutive modeling of porcine tricuspid valve leaflets. J Biomech 2023; 160:111829. [PMID: 37826955 PMCID: PMC10995110 DOI: 10.1016/j.jbiomech.2023.111829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/19/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Biaxial mechanical characterizations are the accepted approach to determine the mechanical response of many biological soft tissues. Although several computational and experimental studies have examined how experimental factors (e.g., clamped vs. suture mounting) affect the acquired tissue mechanical behavior, little is known about the role of specimen dimensions in data acquisition and the subsequent modeling. In this study, we combined our established mechanical characterization framework with an iterative size-reduction protocol to test the hypothesis that specimen dimensions affect the observed mechanical behavior of biaxial characterizations. Our findings indicated that there were non-significant differences in the peak equibiaxial stretches of tricuspid valve leaflets across four specimen dimensions ranging from 4.5×4.5mm to 9 × 9mm. Further analyses revealed that there were significant differences in the low-tensile modulus of the circumferential tissue direction. These differences resulted in significantly different constitutive model parameters for the Tong-Fung model between different specimen dimensions of the posterior and septal leaflets. Overall, our findings demonstrate that specimen dimensions play an important role in experimental characterizations, but not necessarily in constitutive modeling of soft tissue mechanical behavior during biaxial testing with the commercial CellScale BioTester.
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Affiliation(s)
- Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, USA
| | - Shuodao Wang
- School of Mechanical and Aerospace Engineering, Oklahoma State University, USA
| | - Rui Xiao
- Department of Engineering Mechanics, Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Jin Qian
- Department of Engineering Mechanics, Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Arshid Mir
- Department of Pediatrics, University of Oklahoma Health Sciences Center, USA
| | - Harold M Burkhart
- Department of Surgery, University of Oklahoma Health Sciences Center, USA
| | - Gerhard A Holzapfel
- Institute of Biomechanics, Graz University of Technology, Austria; Department of Structural Engineering, Norwegian University of Science and Technology, Norway
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, USA; Institute for Biomedical Engineering, Science and Technology, The University of Oklahoma, USA; Department of Bioengineering, The University of California, Riverside, USA.
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2
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Shao X, Ding Y. Differential evolution for population diversity mechanism based on covariance matrix. ISA TRANSACTIONS 2023; 141:335-350. [PMID: 37429746 DOI: 10.1016/j.isatra.2023.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/10/2023] [Accepted: 06/23/2023] [Indexed: 07/12/2023]
Abstract
Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate CR is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1], LPalmDE [2], PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5], CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed.
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Affiliation(s)
- Xueying Shao
- Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou Key Lab of Advanced Energy Storage and Conversion, Zhejiang Province Key Lab of Leather Engineering, College of Chemistry and Materials Engineering, Wenzhou University, Wenzhou 325035, PR China
| | - Yihong Ding
- Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou Key Lab of Advanced Energy Storage and Conversion, Zhejiang Province Key Lab of Leather Engineering, College of Chemistry and Materials Engineering, Wenzhou University, Wenzhou 325035, PR China.
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Meng Z, Song Z, Shao X, Zhang J, Xu H. FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control. ISA TRANSACTIONS 2023; 139:272-290. [PMID: 37230905 DOI: 10.1016/j.isatra.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.
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Affiliation(s)
- Zhenyu Meng
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
| | - Zhenghao Song
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Xueying Shao
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Junyuan Zhang
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Huarong Xu
- Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China
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Ghasemi M, Zare M, Trojovský P, Zahedibialvaei A, Trojovská E. A hybridizing-enhanced differential evolution for optimization. PeerJ Comput Sci 2023; 9:e1420. [PMID: 37346618 PMCID: PMC10280462 DOI: 10.7717/peerj-cs.1420] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/08/2023] [Indexed: 06/23/2023]
Abstract
Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.
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Affiliation(s)
- Mojtaba Ghasemi
- Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Mohsen Zare
- Department of Electrical Engineering, Jahrom University, Jahrom, Iran
| | - Pavel Trojovský
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
| | - Amir Zahedibialvaei
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Eva Trojovská
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
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5
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Jiang Y, Zhan ZH, Tan KC, Zhang J. Optimizing Niche Center for Multimodal Optimization Problems. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:2544-2557. [PMID: 34919526 DOI: 10.1109/tcyb.2021.3125362] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many real-world optimization problems require searching for multiple optimal solutions simultaneously, which are called multimodal optimization problems (MMOPs). For MMOPs, the algorithm is required both to enlarge population diversity for locating more global optima and to enhance refine ability for increasing the accuracy of the obtained solutions. Thus, numerous niching techniques have been proposed to divide the population into different niches, and each niche is responsible for searching on one or more peaks. However, it is often a challenge to distinguish proper individuals as niche centers in existing niching approaches, which has become a key issue for efficiently solving MMOPs. In this article, the niche center distinguish (NCD) problem is treated as an optimization problem and an NCD-based differential evolution (NCD-DE) algorithm is proposed. In NCD-DE, the niches are formed by using an internal genetic algorithm (GA) to online solve the NCD optimization problem. In the internal GA, a fitness-entropy measurement objective function is designed to evaluate whether a group of niche centers (i.e., encoded by a chromosome in the internal GA) is promising. Moreover, to enhance the exploration and exploitation abilities of NCD-DE in solving the MMOPs, a niching and global cooperative mutation strategy that uses both niche and population information is proposed to generate new individuals. The proposed NCD-DE is compared with some state-of-the-art and recent well-performing algorithms. The experimental results show that NCD-DE achieves better or competitive performance on both the accuracy and completeness of the solutions than the compared algorithms.
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Chakraborty S, Saha AK, Ezugwu AE, Agushaka JO, Zitar RA, Abualigah L. Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:985-1040. [PMID: 36373091 PMCID: PMC9638376 DOI: 10.1007/s11831-022-09825-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.
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Affiliation(s)
- Sanjoy Chakraborty
- Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura India
- Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, KwaZulu-Natal 3201 South Africa
| | - Jeffrey O. Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, KwaZulu-Natal 3201 South Africa
- Department of Computer Science, Federal University of Lafia, Lafia, 950101 Nigeria
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044 Abu Dhabi, United Arab Emirates
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
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7
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Pineda-Castillo SA, Aparicio-Ruiz S, Burns MM, Laurence DW, Bradshaw E, Gu T, Holzapfel GA, Lee CH. Linking the region-specific tissue microstructure to the biaxial mechanical properties of the porcine left anterior descending artery. Acta Biomater 2022; 150:295-309. [PMID: 35905825 PMCID: PMC10230544 DOI: 10.1016/j.actbio.2022.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022]
Abstract
Coronary atherosclerosis is the main cause of death worldwide. Advancing the understanding of coronary microstructure-based mechanics is fundamental for the development of therapeutic tools and surgical procedures. Although the passive biaxial properties of the coronary arteries have been extensively explored, their regional differences and the relationship between tissue microstructure and mechanics have not been fully characterized. In this study, we characterized the passive biaxial mechanical properties and microstructural properties of the proximal, medial, and distal regions of the porcine left anterior descending artery (LADA). We also attempted to relate the biaxial stress-stretch response of the LADA and its respective birefringent responses to the polarized light for obtaining information about the load-dependent microstructural variations. We found that the LADA extensibility is reduced in the proximal-to-distal direction and that the medial region exhibits more heterogeneous mechanical behavior than the other two regions. We have also observed highly dynamic microstructural behavior where fiber families realign themselves depending on loading. In addition, we found that the microstructure of the distal region exhibited highly aligned fibers along the longitudinal axis of the artery. To verify this microstructural feature, we imaged the LADA specimens with multi-photon microscopy and observed that the adventitia microstructure transitioned from a random fiber network in the proximal region to highly aligned fibers in the distal region. Our findings could offer new perspectives for understanding coronary mechanics and aid in the development of tissue-engineered vascular grafts, which are currently limited due to their mismatch with native tissue in terms of mechanical properties and microstructural features. STATEMENT OF SIGNIFICANCE: The tissue biomechanics of coronary arteries is fundamental for the development of revascularization techniques such as coronary artery bypass. These therapeutics require a deep understanding of arterial mechanics, microstructure, and mechanobiology to prevent graft failure and reoperation. The present study characterizes the unique regional mechanical and microstructural properties of the porcine left anterior descending artery using biaxial testing, polarized-light imaging, and confocal microscopy. This comprehensive characterization provides an improved understanding of the collagen/elastin architecture in response to mechanical loads using a region-specific approach. The unique tissue properties obtained from this study will provide guidance for the selection of anastomotic sites in coronary artery bypass grafting and for the design of tissue-engineered vascular grafts.
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Affiliation(s)
- Sergio A Pineda-Castillo
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA; Stephenson School of Biomedical Engineering, The University of Oklahoma, USA
| | - Santiago Aparicio-Ruiz
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA
| | - Madison M Burns
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA
| | - Devin W Laurence
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA
| | - Elizabeth Bradshaw
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA
| | - Tingting Gu
- Samuel Roberts Noble Microscopy Laboratory, The University of Oklahoma, USA
| | - Gerhard A Holzapfel
- Institute of Biomechanics, Graz University of Technology, Austria; Department of Structural Engineering, Norwegian University of Science and Technology, Norway
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Lab, School of Aerospace and Mechanical Engineering, The University of Oklahoma, USA.
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8
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Gupta S, Su R. An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109280] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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9
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10
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Self-adaptive salp swarm algorithm for optimization problems. Soft comput 2022. [DOI: 10.1007/s00500-022-07280-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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11
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Adaptive Differential Evolution Algorithm Based on Fitness Landscape Characteristic. MATHEMATICS 2022. [DOI: 10.3390/math10091511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm’s ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified.
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He X, Taneja K, Chen JS, Lee CH, Hodgson J, Malis V, Sinha U, Sinha S. Multiscale modeling of passive material influences on deformation and force output of skeletal muscles. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3571. [PMID: 35049153 DOI: 10.1002/cnm.3571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/06/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
Passive materials in human skeletal muscle tissues play an important role in force output of skeletal muscles. This paper introduces a multiscale modeling framework to investigate how age-associated variations on microscale passive muscle components, including microstructural geometry (e.g., connective tissue thickness) and material properties (e.g., anisotropy), influence the force output and deformations of the continuum skeletal muscle. We first define a representative volume element (RVE) for the microstructure of muscle and determine the homogenized macroscale mechanical properties of the RVE from the separate mechanical properties of the individual components of the RVE, including muscle fibers and connective tissue with its associated collagen fibers. The homogenized properties of the RVE are then used to define the elements of the continuum muscle model to evaluate the force output and deformations of the whole muscle. Conversely, the regional deformations of the continuum model are fed back to the RVE model to determine the responses of the individual microscale components. Simulations of muscle isometric contractions at a range of muscle lengths are performed to investigate the effects of muscle architectural changes (e.g., pennation angles) due to aging on force output and muscle deformation. The correlations between the pennation angle, the shear deformation in the microscale connective tissue (an indicator for the lateral force transmission), the angle difference between the fiber direction and principal strain direction and the resulting shear deformation at the continuum scale, as well as the force output of the skeletal muscle are also discussed.
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Affiliation(s)
- Xiaolong He
- Department of Structural Engineering, University of California San Diego, San Diego, California, USA
| | - Karan Taneja
- Department of Structural Engineering, University of California San Diego, San Diego, California, USA
| | - Jiun-Shyan Chen
- Department of Structural Engineering, University of California San Diego, San Diego, California, USA
| | - Chung-Hao Lee
- School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, Oklahoma, USA
| | - John Hodgson
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, USA
| | - Vadim Malis
- Department of Physics, University of California San Diego, San Diego, California, USA
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Usha Sinha
- Department of Physics, San Diego State University, San Diego, California, USA
| | - Shantanu Sinha
- Department of Radiology, University of California San Diego, San Diego, California, USA
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Chen Q, Ding J, Chai T, Pan Q. Evolutionary Optimization Under Uncertainty: The Strategies to Handle Varied Constraints for Fluid Catalytic Cracking Operation. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2249-2262. [PMID: 32721907 DOI: 10.1109/tcyb.2020.3005893] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.
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14
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Yan X, Tian M. Differential evolution with two-level adaptive mechanism for numerical optimization. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Aytaç UC, Güneş A, Ajlouni N. A novel adaptive momentum method for medical image classification using convolutional neural network. BMC Med Imaging 2022; 22:34. [PMID: 35232390 PMCID: PMC8886705 DOI: 10.1186/s12880-022-00755-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/07/2022] [Indexed: 12/25/2022] Open
Abstract
Background AI for medical diagnosis has made a tremendous impact by applying convolutional neural networks (CNNs) to medical image classification and momentum plays an essential role in stochastic gradient optimization algorithms for accelerating or improving training convolutional neural networks. In traditional optimizers in CNNs, the momentum is usually weighted by a constant. However, tuning hyperparameters for momentum can be computationally complex. In this paper, we propose a novel adaptive momentum for fast and stable convergence. Method Applying adaptive momentum rate proposes increasing or decreasing based on every epoch's error changes, and it eliminates the need for momentum hyperparameter optimization. We tested the proposed method with 3 different datasets: REMBRANDT Brain Cancer, NIH Chest X-ray, COVID-19 CT scan. We compared the performance of a novel adaptive momentum optimizer with Stochastic gradient descent (SGD) and other adaptive optimizers such as Adam and RMSprop. Results Proposed method improves SGD performance by reducing classification error from 6.12 to 5.44%, and it achieved the lowest error and highest accuracy compared with other optimizers. To strengthen the outcomes of this study, we investigated the performance comparison for the state-of-the-art CNN architectures with adaptive momentum. The results shows that the proposed method achieved the highest with 95% compared to state-of-the-art CNN architectures while using the same dataset. The proposed method improves convergence performance by reducing classification error and achieves high accuracy compared with other optimizers.
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Affiliation(s)
- Utku Can Aytaç
- Computer Engineering Department, Faculty of Computer Engineering, Istanbul Aydın University, Besyol, Inonu Cd. No: 38, 34295, Kucukcekmece, Istanbul, Turkey.
| | - Ali Güneş
- Computer Engineering Department, Istanbul Aydin University, Istanbul, Turkey
| | - Naim Ajlouni
- Computer Engineering Department, Istanbul Atlas University, Istanbul, Turkey
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16
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Online algorithm configuration for differential evolution algorithm. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02752-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Li G, Li Q, Wang Z. Adaptive Differential Evolution Algorithm with Multiple Gaussian Learning Models. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20503-3_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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18
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Laurence DW, Lee CH. Determination of a Strain Energy Density Function for the Tricuspid Valve Leaflets Using Constant Invariant-Based Mechanical Characterizations. J Biomech Eng 2021; 143:1120829. [PMID: 34596679 DOI: 10.1115/1.4052612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Indexed: 11/08/2022]
Abstract
The tricuspid valve (TV) regulates the blood flow within the right side of the heart. Despite recent improvements in understanding TV mechanical and microstructural properties, limited attention has been devoted to the development of TV-specific constitutive models. The objective of this work is to use the first-of-its-kind experimental data from constant invariant-based mechanical characterizations to determine a suitable invariant-based strain energy density function (SEDF). Six specimens for each TV leaflet are characterized using constant invariant mechanical testing. The data is then fit with three candidate SEDF forms: (i) a polynomial model-the transversely isotropic version of the Mooney-Rivlin model, (ii) an exponential model, and (iii) a combined polynomial-exponential model. Similar fitting capabilities were found for the exponential and the polynomial forms (R2=0.92-0.99 versus 0.91-0.97) compared to the combined polynomial-exponential SEDF (R2=0.65-0.95). Furthermore, the polynomial form had larger Pearson's correlation coefficients than the exponential form (0.51 versus 0.30), indicating a more well-defined search space. Finally, the exponential and the combined polynomial-exponential forms had notably smaller but more eccentric model parameter's confidence regions than the polynomial form. Further evaluations of invariant decoupling revealed that the decoupling of the invariant terms within the exponential form leads to a less satisfactory performance. From these results, we conclude that the exponential form is better suited for the TV leaflets owing to its superb fitting capabilities and smaller parameter's confidence regions.
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Affiliation(s)
- Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK 73019
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, 865 Asp Avenue, Felgar Hall 219C, Norman, OK 73019; Institute for Biomedical Engineering, Science and Technology, The University of Oklahoma, 865 Asp Avenue, Felgar Hall 219C, Norman, OK 73019
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Dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques for differential evolution. Soft comput 2021. [DOI: 10.1007/s00500-021-06454-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Hudson LT, Laurence DW, Lau HM, Mullins BT, Doan DD, Lee CH. Linking collagen fiber architecture to tissue-level biaxial mechanical behaviors of porcine semilunar heart valve cusps. J Mech Behav Biomed Mater 2021; 125:104907. [PMID: 34736023 DOI: 10.1016/j.jmbbm.2021.104907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 01/13/2023]
Abstract
The semilunar heart valves regulate the blood flow from the ventricles to the major arteries through the opening and closing of the scallop shaped cusps. These cusps are composed of collagen fibers that act as the primary loading-bearing component. The load-dependent collagen fiber architecture has been previously examined in the existing literature; however, these studies relied on chemical clearing and tissue modifications to observe the underlying changes in response to mechanical loads. In the present study, we address this gap in knowledge by quantifying the collagen fiber orientations and alignments of the aortic and pulmonary cusps through a multi-scale, non-destructive experimental approach. This opto-mechanical approach, which combines polarized spatial frequency domain imaging and biaxial mechanical testing, provides a greater field of view (10-25mm) and faster imaging time (45-50s) than other traditional collagen imaging techniques. The birefringent response of the collagen fibers was fit with a von Mises distribution, while the biaxial mechanical testing data was implemented into a modified full structural model for further analysis. Our results showed that the semilunar heart valve cusps are more extensible in the tissue's radial direction than the circumferential direction under all the varied biaxial testing protocols, together with greater material anisotropy among the pulmonary valve cusps compared to the aortic valve cusps. The collagen fibers were shown to reorient towards the direction of the greatest applied loading and incrementally realign with the increased applied stress. The collagen fiber architecture within the aortic valve cusps were found to be more homogeneous than the pulmonary valve counterparts, reflecting the differences in the physiological environments experienced by these two semilunar heart valves. Further, the von Mises distribution fitting highlighted the presence and contribution of two distinct fiber families for each of the two semilunar heart valves. The results from this work would provide valuable insight into connecting tissue-level mechanics to the underlying collagen fiber architecture-an essential information for the future development of high-fidelity aortic/pulmonary valve computational models.
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Affiliation(s)
- Luke T Hudson
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA
| | - Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA
| | - Hunter M Lau
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA
| | - Brennan T Mullins
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA
| | - Deenna D Doan
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory, The University of Oklahoma, Norman, OK, 73019, USA; Institute for Biomedical Engineering, Science and Technology (IBEST), The University of Oklahoma, USA.
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Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.
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Q-Learning-based parameter control in differential evolution for structural optimization. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107464] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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A backtracking differential evolution with multi-mutation strategies autonomy and collaboration. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02577-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Meng Z, Yang C. Hip-DE: Historical population based mutation strategy in differential evolution with parameter adaptive mechanism. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang ZJ, Zhan ZH, Kwong S, Jin H, Zhang J. Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1175-1188. [PMID: 32224474 DOI: 10.1109/tcyb.2020.2977956] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results show that AGLDPSO performs better than or at least comparable with some other state-of-the-art large-scale optimization algorithms, even the winner of the competition on large-scale optimization, on all the 35 benchmark functions from both IEEE Congress on Evolutionary Computation (IEEE CEC2010) and IEEE CEC2013 large-scale optimization test suites.
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Laurence DW, Homburg H, Yan F, Tang Q, Fung KM, Bohnstedt BN, Holzapfel GA, Lee CH. A pilot study on biaxial mechanical, collagen microstructural, and morphological characterizations of a resected human intracranial aneurysm tissue. Sci Rep 2021; 11:3525. [PMID: 33568740 PMCID: PMC7876029 DOI: 10.1038/s41598-021-82991-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 01/25/2021] [Indexed: 02/08/2023] Open
Abstract
Intracranial aneurysms (ICAs) are focal dilatations that imply a weakening of the brain artery. Incidental rupture of an ICA is increasingly responsible for significant mortality and morbidity in the American’s aging population. Previous studies have quantified the pressure-volume characteristics, uniaxial mechanical properties, and morphological features of human aneurysms. In this pilot study, for the first time, we comprehensively quantified the mechanical, collagen fiber microstructural, and morphological properties of one resected human posterior inferior cerebellar artery aneurysm. The tissue from the dome of a right posterior inferior cerebral aneurysm was first mechanically characterized using biaxial tension and stress relaxation tests. Then, the load-dependent collagen fiber architecture of the aneurysm tissue was quantified using an in-house polarized spatial frequency domain imaging system. Finally, optical coherence tomography and histological procedures were used to quantify the tissue’s microstructural morphology. Mechanically, the tissue was shown to exhibit hysteresis, a nonlinear stress-strain response, and material anisotropy. Moreover, the unloaded collagen fiber architecture of the tissue was predominantly aligned with the testing Y-direction and rotated towards the X-direction under increasing equibiaxial loading. Furthermore, our histological analysis showed a considerable damage to the morphological integrity of the tissue, including lack of elastin, intimal thickening, and calcium deposition. This new unified characterization framework can be extended to better understand the mechanics-microstructure interrelationship of aneurysm tissues at different time points of the formation or growth. Such specimen-specific information is anticipated to provide valuable insight that may improve our current understanding of aneurysm growth and rupture potential.
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Affiliation(s)
- Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory (BBDL), School of Aerospace and Mechanical Engineering, The University of Oklahoma, 865 Asp Ave., Felgar Hall 219C, Norman, 73019, USA
| | - Hannah Homburg
- Department of Neurosurgery, The University of Oklahoma Health Sciences Center, Oklahoma City, 73104, USA
| | - Feng Yan
- Biophotonic Imaging Laboratory, Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, 73019, USA
| | - Qinggong Tang
- Biophotonic Imaging Laboratory, Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, 73019, USA
| | - Kar-Ming Fung
- Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City, 73104, USA.,Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, 73104, USA
| | - Bradley N Bohnstedt
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Gerhard A Holzapfel
- Institute of Biomechanics, Graz University of Technology, 8010, Graz, Austria.,Department of Structural Engineering, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory (BBDL), School of Aerospace and Mechanical Engineering, The University of Oklahoma, 865 Asp Ave., Felgar Hall 219C, Norman, 73019, USA. .,Institute for Biomedical Engineering, Science and Technology, The University of Oklahoma, Norman, OK, 73019, USA.
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Sun G, Li C, Deng L. An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05708-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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JDF-DE: a differential evolution with Jrand number decreasing mechanism and feedback guide technique for global numerical optimization. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01795-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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30
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He Q, Laurence DW, Lee CH, Chen JS. Manifold learning based data-driven modeling for soft biological tissues. J Biomech 2020; 117:110124. [PMID: 33515902 DOI: 10.1016/j.jbiomech.2020.110124] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 07/16/2020] [Accepted: 11/03/2020] [Indexed: 02/08/2023]
Abstract
Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material's response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation biological tissues. Herein, we extend our recent local convexity data-driven (LCDD) framework (He and Chen, 2020) to model the mechanical response of a porcine heart mitral valve posterior leaflet. The predictability of the LCDD framework by using various combinations of biaxial and pure shear training protocols are investigated, and its effectiveness is compared with a full structural, phenomenological model modified from Zhang et al. (2016) and a continuum phenomenological Fung-type model (Tong and Fung, 1976). We show that the predictivity of the proposed LCDD nonlinear solver is generally less sensitive to the type of loading protocols (biaxial and pure shear) used in the data set, while more sensitive to the insufficient coverage of the experimental data when compared to the predictivity of the two selected phenomenological models. While no pre-defined functional form in the material model is necessary in LCDD, this study reinstates the importance of having sufficiently rich data coverage in the date-driven and machine learning type of approaches. It is also shown that the proposed LCDD method is an enhancement over the earlier distance-minimization data-driven (DMDD) against noisy data. This study demonstrates that when sufficient data is available, data-driven computing can be an alternative method for modeling complex biological materials.
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Affiliation(s)
- Qizhi He
- Department of Structural Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA; Institute for Biomedical Engineering, Science and Technology, The University of Oklahoma, Norman, OK 73019, USA
| | - Jiun-Shyan Chen
- Department of Structural Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
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31
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Mu L, Wang P, Xin G. Quantum-inspired algorithm with fitness landscape approximation in reduced dimensional spaces for numerical function optimization. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.03.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhou XG, Peng CX, Liu J, Zhang Y, Zhang GJ. Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION : A PUBLICATION OF THE IEEE NEURAL NETWORKS COUNCIL 2020; 24:536-550. [PMID: 33603321 PMCID: PMC7885903 DOI: 10.1109/tevc.2019.2938531] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This paper presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with a self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3D structure. Experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors.
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Affiliation(s)
- Xiao-Gen Zhou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China, and also with the Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jun Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, and also with the Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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Liu R, Ren R, Liu J, Liu J. A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106120] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Pelusi D, Mascella R, Tallini L, Nayak J, Naik B, Deng Y. An Improved Moth-Flame Optimization algorithm with hybrid search phase. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105277] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Tanabe R, Fukunaga A. Reviewing and Benchmarking Parameter Control Methods in Differential Evolution. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:1170-1184. [PMID: 30703058 DOI: 10.1109/tcyb.2019.2892735] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Many differential evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral component of a complex DE algorithm. Thus, the characteristics and performance of each method are poorly understood. We present an in-depth review of 24 PCMs for the scale factor and crossover rate in DE and a large-scale benchmarking study. We carefully extract the 24 PCMs from their original, complex algorithms and describe them according to a systematic manner. Our review facilitates the understanding of similarities and differences between existing, representative PCMs. The performance of DEs with the 24 PCMs and 16 variation operators is investigated on 24 black-box benchmark functions. Our benchmarking results reveal which methods exhibit high performance when embedded in a standardized framework under 16 different conditions, independent from their original, complex algorithms. We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.
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36
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Performance-driven adaptive differential evolution with neighborhood topology for numerical optimization. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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37
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Differential evolutionary algorithm with an evolutionary state estimation method and a two-level selection mechanism. Soft comput 2019. [DOI: 10.1007/s00500-019-04621-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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38
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Li Y, Wang S. Differential evolution algorithm with elite archive and mutation strategies collaboration. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09786-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Zhan ZH, Wang ZJ, Jin H, Zhang J. Adaptive Distributed Differential Evolution. IEEE TRANSACTIONS ON CYBERNETICS 2019; 50:4633-4647. [PMID: 31634855 DOI: 10.1109/tcyb.2019.2944873] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants.
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Du H, Wang Z, Fan Y, Li C, Yao J. Dual-Subpopulation as reciprocal optional external archives for differential evolution. PLoS One 2019; 14:e0222103. [PMID: 31536535 PMCID: PMC6752808 DOI: 10.1371/journal.pone.0222103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 08/21/2019] [Indexed: 11/19/2022] Open
Abstract
Differential Evolution (DE) is powerful for global optimization problems. Among DE algorithms, JADE and its variants, whose mutation strategy is DE/current-to-pbest/1 with optional archive, have good performance. A significant feature of the above mutation strategy is that one individual for difference operation comes from the union of the optional external archive and the population. In existing DE algorithms based on the mutation strategy—JADE and its variants, individuals eliminated from the population are send to the archive. In this paper, we propose a scheme for managing the optional external archive. According to our scheme, two subpopulations are maintained in the population. Each of them regards the other as the archive. In experiments, our scheme is applied in JADE and two of its variants—SHADE and L-SHADE. Experimental results show that our scheme can enhance JADE and its variants. Moreover, it can be seen that L-SHADE with our scheme performs significantly better than four DE algorithms, CoBiDE, MPEDE, EDEV, and MLCCDE.
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Affiliation(s)
- Haiming Du
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
- * E-mail:
| | - Zaichao Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
| | - Yiqun Fan
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
| | - Chengjun Li
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
| | - Juan Yao
- College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China
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Accelerating differential evolution based on a subset-to-subset survivor selection operator. Soft comput 2019. [DOI: 10.1007/s00500-018-3060-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Abstract
The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.
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Zhou XG, Zhang GJ. Differential Evolution With Underestimation-Based Multimutation Strategy. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:1353-1364. [PMID: 29994744 DOI: 10.1109/tcyb.2018.2801287] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
As we know, the performance of differential evolution (DE) highly depends on the mutation strategy. However, it is difficult to choose a suitable mutation strategy for a specific problem or different running stages. This paper proposes an underestimation-based multimutation strategy (UMS) for DE. In the UMS, a set of candidate offsprings are simultaneously generated for each target individual by utilizing multiple mutation strategies. Then a cheap abstract convex underestimation model is built based on some selected individuals to obtain the underestimation value of each candidate offspring. According to the quality of each candidate offspring measured by the underestimation value, the most promising candidate solution is chosen as the offspring. Compared to the existing probability-based multimutation techniques, no mutation strategies are lost during the search process as each mutation strategy has the same probability to generate a candidate solution. Moreover, no extra function evaluations are produced because the candidate solutions are filtered by the underestimation value. The UMS is integrated into some DE variants and compared with their original algorithms and several advanced DE approaches over the CEC 2013 and 2014 benchmark sets. Additionally, a well-known real-world problem is employed to evaluate the performance of the UMS. Experimental results show that the proposed UMS can improve the performance of the advanced DE variants.
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An enhanced utilization mechanism of population information for Differential evolution. EVOLUTIONARY INTELLIGENCE 2018. [DOI: 10.1007/s12065-018-0181-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Yu X, Chen WN, Gu T, Zhang H, Yuan H, Kwong S, Zhang J. Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:2139-2153. [PMID: 28792909 DOI: 10.1109/tcyb.2017.2728120] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.
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