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Bernardo RM, Torres DF, Herdeiro CA, Soares dos Santos MP. Universe-inspired algorithms for control engineering: A review. Heliyon 2024; 10:e31771. [PMID: 38882329 PMCID: PMC11176799 DOI: 10.1016/j.heliyon.2024.e31771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 06/18/2024] Open
Abstract
Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: Gravitational Search Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universe-inspired algorithms on control problems of dynamic systems, providing an extensive list of control-related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation-inspired and black-hole dynamic-driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high-sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control-astrophysics bridges can be established and applied in a wide range of applications.
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Affiliation(s)
- Rodrigo M.C. Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Delfim F.M. Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Carlos A.R. Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Marco P. Soares dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
- Intelligent Systems Associate Laboratory (LASI), Portugal
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Levy flight incorporated hybrid learning model for gravitational search algorithm. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Kundu T, Garg H. INNA: An improved neural network algorithm for solving reliability optimization problems. Neural Comput Appl 2022; 34:20865-20898. [PMID: 35937044 PMCID: PMC9340737 DOI: 10.1007/s00521-022-07565-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/28/2022] [Indexed: 11/25/2022]
Abstract
The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.
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Affiliation(s)
- Tanmay Kundu
- Department of Mathematics, Chandigarh University, Mohali, Punjab 140413 India
| | - Harish Garg
- School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab 147004 India
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4
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Joshi SK. Chaos embedded opposition based learning for gravitational search algorithm. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03786-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kundu T, Deepmala, Jain PK. A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems. APPL INTELL 2022; 52:12630-12667. [PMID: 36161208 PMCID: PMC9481865 DOI: 10.1007/s10489-021-02862-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2021] [Indexed: 12/23/2022]
Abstract
A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.
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FB-GSA: A fuzzy bi-level programming based gravitational search algorithm for unconstrained optimization. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01884-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Joshi SK, Gopal A, Singh S, Nagar AK, Bansal JC. A novel neighborhood archives embedded gravitational constant in GSA. Soft comput 2021. [DOI: 10.1007/s00500-021-05648-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Pelusi D, Mascella R, Tallini L, Nayak J, Naik B, Deng Y. Improving exploration and exploitation via a Hyperbolic Gravitational Search Algorithm. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105404] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Chen K, Xue B, Zhang M, Zhou F. Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105568] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Hakli H, Kiran MS. An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01094-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Alirezanejad M, Enayatifar R, Motameni H, Nematzadeh H. GSA-LA: gravitational search algorithm based on learning automata. J EXP THEOR ARTIF IN 2020. [DOI: 10.1080/0952813x.2020.1725650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Mehdi Alirezanejad
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Rasul Enayatifar
- Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
| | - Homayun Motameni
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Hossein Nematzadeh
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
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Kahraman HT, Aras S, Gedikli E. Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105169] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Zhang Y, Jin Z, Chen Y. Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.07.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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16
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Zhang Y, Jin Z, Chen Y. Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04580-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles. REMOTE SENSING 2019. [DOI: 10.3390/rs11080952] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.
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Pan X, Gao L, Zhang B, Yang F, Liao W. High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network. SENSORS 2018; 18:s18113774. [PMID: 30400591 PMCID: PMC6263496 DOI: 10.3390/s18113774] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 10/31/2018] [Accepted: 11/01/2018] [Indexed: 11/25/2022]
Abstract
Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline.
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Affiliation(s)
- Xuran Pan
- Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
| | - Lianru Gao
- Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
| | - Bing Zhang
- Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
| | - Fan Yang
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
| | - Wenzhi Liao
- Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium.
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