Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

Structural optimization of different truss designs using two archive mult objective crystal structure optimization algorithm

Optimizing a multi-objective structure is a challenging design problem that requires handling several competing goals and constraints. Despite their success in resolving such issues, metaheuristics can be difficult to apply due to their stochastic nature and restrictions. This work proposes the multi-objective crystal structure optimizer (MOCRY), a potent and effective optimizer, to address this problem. The MOCRY algorithm, also known as MOCRY2arc, is built on a two-archive idea centered on diversity and convergence, respectively. The efficacy of MOCRY2arc in solving five typical planar and spatial real-world structure optimization issues was assessed. Because of these problems, safety and size limits were put on discrete cross-sectional regions and component stress. At the same time, different goals were being pursued, such as making nodal points bend more and reducing the mass of trusses. Four recognized standard evaluators-Hypervolume (HV), Generational-Inverted Generational Distance (GD, IGD), Spacing to Extent Metrics (STE), convergence, and diversity plots-were utilized to compare the results with those of nine sophisticated optimization techniques, including MOCRY and NSGA-II. Moreover, the Friedman rank test and comparison analysis showed that MOCRY2arc performed better at resolving big structure optimization issues in a shorter amount of computing time. In addition to identifying and realizing effective Pareto-optimal sets, the recommended method produced strong variety and convergence in the objective and choice spaces. As a result, MOCRY2arc may be a useful tool for handling challenging multi-objective structure optimization issues.

A parrot optimizer for solving multiobjective design sensor placement in helicopter main rotor blade

Various sectors and applications, including machine learning, data mining, operations research, economical problem, and science, can be structured as multi-objective optimization problems. This study introduces a novel multi-objective algorithm based on the recently developed parrot optimizer (PO) called MOPO. An external repository matrix i.e. "archive" is incorporated with the PO so that maintain the Pareto optimal solutions achieved. The MOPO utilizes the elitist non-dominated sorting, to maintain the diversity among the optimal set of solutions, further the mutate-leaders strategy is proposed to to strengthen the diversity of obtained Pareto solutions and mitigates the risk of local minima. The efficacy of the MOPO is assessed through optimizing two categories of multi-objective, include twenty benchmark test suite from the IEEE CEC'20, and real-world multi-objective design challenge, through optimizing the sensor placement in helicopter main rotor blade. The MOPO is compared against nine well-known, recent and robust multi-objective optimization algorithms. Various quantative and qualitative metrics are employed to conduct a comprehensive examination of the results; further the Friedman test and Wilcoxon test are applied on results of the four performance metrics i.e. PSP, HV, IGDf and IDGX, it demonstrates that the MOPO performed comparably to other algorithms on the most test methods, and achieved the first rank among other competitors. The Wilcoxon test exhibit the significant variance of MOPO rather competitors on p-value = 0.05. The MOPO takes average execution time less than MOSMA, SPEA2, MOPSO by 20% rate.

An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project

In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while innovatively combining the fast non-dominated sorting technique to effectively evaluate and approximate the Pareto optimal solution set. To enhance the diversity and generalization of the solution set, the crowding distance mechanism is introduced, ensuring a good balance between multiple optimization objectives and a wider coverage of the solution space. Additionally, an acceleration factor based on trigonometric functions and an adaptive Gaussian mutation strategy are incorporated, improving the exploration ability of the particles in the search space and facilitating their movement towards the global optimal solution more effectively. The performance of the algorithm is verified using the multi-modal multi-objective benchmark function set provided by CEC2020, and comparisons are made with five advanced multi-objective metaheuristics. The MOIPSO algorithm is also applied to solve the design problem of rail transit upper cover foundation pit, further demonstrating the practical effectiveness of the proposed algorithm. The results show that MOIPSO not only performs well in multi-objective function testing but also proves highly competitive in solving real-world engineering problems. Note that the source codes of MOGWO are publicly available at https://au.mathworks.com/matlabcentral/fileexchange/177404-moipso-optimization-engineering-problem .

Advanced methodology for maximum torque point tracking of hybrid excitation PMSM for EVs

This manuscript presents an innovative control strategy for the Hybrid Excitation Permanent Magnet Synchronous Motor (HEPMSM) designed for electric vehicle (EV) applications. The strategy combines Maximum Torque Point Tracking (MTPT) and Maximum Torque Per Ampere (MTPA) techniques to track the ideal torque-speed profile, ensuring maximum torque at low speeds for starting and climbing, and high power at higher speeds for cruising. A novel unidirectional excitation current method is proposed to replace traditional bidirectional field current control, eliminating the risk of permanent magnet demagnetization, reducing copper losses, and increasing efficiency. This approach extends the constant power (CP) region by a 4.2:1 ratio. The manuscript also introduces a detailed mathematical model, considering both iron core losses and their impact on the EV profile. Additionally, the Multi-Objective Ant Lion Optimizer (MOALO) algorithm is used in two stages: first to optimize the hybridization ratio (HR) and base speed (Nb), and second to analyze the effect of varying the hybridization ratio while maintaining constrained output power. The proposed strategy is validated through MATLAB simulations, demonstrating its effectiveness in achieving high acceleration, efficiency, and reliability for EV applications.

An Efficient Multi-Objective White Shark Algorithm

To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes.

An efficient multi-objective parrot optimizer for global and engineering optimization problems

The Parrot Optimizer (PO) has recently emerged as a powerful algorithm for single-objective optimization, known for its strong global search capabilities. This study extends PO into the Multi-Objective Parrot Optimizer (MOPO), tailored for multi-objective optimization (MOO) problems. MOPO integrates an outward archive to preserve Pareto optimal solutions, inspired by the search behavior of Pyrrhura Molinae parrots. Its performance is validated on the Congress on Evolutionary Computation 2020 (CEC'2020) multi-objective benchmark suite. Additionally, extensive testing on four constrained engineering design challenges and eight popular confined and unconstrained test cases proves MOPO's superiority. Moreover, the real-world multi-objective optimization of helical coil springs for automotive applications is conducted to depict the reliability of the proposed MOPO in solving practical problems. Comparative analysis was performed with seven recently published, state-of-the-art algorithms chosen for their proven effectiveness and representation of the current research landscape-Improved Multi-Objective Manta-Ray Foraging Optimization (IMOMRFO), Multi-Objective Gorilla Troops Optimizer (MOGTO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Whale Optimization Algorithm (MOWOA), Multi-Objective Slime Mold Algorithm (MOSMA), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The results indicate that MOPO consistently outperforms these algorithms across several key metrics, including Pareto Set Proximity (PSP), Inverted Generational Distance in Decision Space (IGDX), Hypervolume (HV), Generational Distance (GD), spacing, and maximum spread, confirming its potential as a robust method for addressing complex MOO problems.

Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria

Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables.

MOANA: Multi-objective ant nesting algorithm for optimization problems

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.

Differences in brain spindle density during sleep between patients with and without type 2 diabetes

BACKGROUND

Sleep spindles may be implicated in sensing and regulation of peripheral glucose. Whether spindle density in patients with type 2 diabetes mellitus (T2DM) differs from that of healthy subjects is unknown.

METHODS

Our retrospective analysis of polysomnography (PSG) studies identified 952 patients with T2DM and 952 sex-, age- and BMI-matched control subjects. We extracted spindles from PSG electroencephalograms and used rank-based statistical methods to test for differences between subjects with and without diabetes. We also explored potential modifiers of spindle density differences. We replicated our analysis on independent data from the Sleep Heart Health Study.

RESULTS

We found that patients with T2DM exhibited about half the spindle density during sleep as matched controls (P < 0.0001). The replication dataset showed similar trends. The patient-minus-control paired difference in spindle density for pairs where the patient had major complications were larger than corresponding paired differences in pairs where the patient lacked major complications, despite both patient groups having significantly lower spindle density compared to their respective control subjects. Patients with a prescription for a glucagon-like peptide 1 receptor agonist had significantly higher spindle density than those without one (P ≤ 0.03). Spindle density in patients with T2DM monotonically decreased as their highest recorded HbA1C level increased (P ≤ 0.003).

CONCLUSIONS

T2DM patients had significantly lower spindle density than control subjects; the size of that difference was correlated with markers of disease severity (complications and glycemic control). These findings expand our understanding of the relationships between sleep and glucose regulation.

Multi-objective optimization of a triple-eccentric butterfly valve considering structural safety and sealing performance

The structural safety and sealing performance of a triple-eccentric butterfly valve are crucial technical indicators that influence its reliability and service life. In this study, a new multi-objective optimization strategy is proposed to realize a lightweight design of valve trims, reduce the maximum equivalent stress, and reasonably distribute the sealing-specific pressure. A two-stage optimization scheme is designed by combining topology optimization (TO) and response surface methodology optimization (RSM). The topology optimization is employed to allocate the material distribution of the valve trims and provide the parameters for the response surface optimization, while the response surface methodology optimization conducts a further revision and optimization of the structural parameters of the valve trims. The results of the simulation experiments indicate that the maximum equivalent stress of the lightweight designed valve trims is reduced from 290.85 MPa to 99.88 MPa, and the maximum sealing-specific pressure of the sealing surface is reduced from 197.78 MPa to 77.83 MPa. Additionally, a novel approach is presented for assessing the sealing performance using the clearance of the fitting surface. This method can intuitively evaluate the state of metal sealing and guide the design of the fitting tolerance by analyzing the sensitivity of the dimensional deviation to the sealing-specific pressure. The findings demonstrate that the optimized valve exhibits good structural safety and sealing performance.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the "No Free Lunch theorem". Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

Multi-objective resistance-capacitance optimization algorithm: An effective multi-objective algorithm for engineering design problems

Focusing on practical engineering applications, this study introduces the Multi-Objective Resistance-Capacitance Optimization Algorithm (MORCOA), a new approach for multi-objective optimization problems. MORCOA uses the transient response behaviour of resistance-capacitance circuits to navigate complex optimization landscapes and identify global optima when faced with many competing objectives. The core approach of MORCOA combines a dynamic elimination-based crowding distance mechanism with non-dominated sorting to generate an ideal and evenly distributed Pareto front. The algorithm's effectiveness is evaluated through a structured, three-phase analysis. Initially, MORCOA is applied to five benchmark problems from the ZDT test suite, with performance assessed using various metrics and compared against state-of-the-art multi-objective optimization techniques. The study then expands to include seven problems from the DTLZ benchmark collection, further validating MORCOA's effectiveness. The final phase involves applying MORCOA to six real-world constrained engineering design problems. Notably, the optimization of a honeycomb heat sink, which is crucial in thermal management systems, is a significant part of this study. This phase uses a range of performance measures to assess MORCOA's practical application and efficiency in engineering design. The results highlight MORCOA's robustness and efficiency in both real-world engineering applications and benchmark problems, demonstrating its superior capabilities compared to existing algorithms. The effective use of MORCOA in real-world engineering design problems indicates its potential as an adaptable and powerful tool for complex multi-objective optimization tasks.

Unlocking synergies of drawdown operation: Multi-objective optimization of reservoir emergency storage capacity

Optimizing reservoir drawdown operations holds significant implications for hydropower generation, water supply, and drought mitigation strategies. However, achieving multi-objective optimization in reservoir drawdown operations poses fundamental challenges, particularly considering emergency storage capacity and seasonal drought patterns. This study introduces a novel multi-objective optimization framework tailored for a mega reservoir, focusing on drawdown operations to enhance hydropower generation and water supply reliability. A drawdown operation model leveraging a multi-objective ant lion optimizer is developed to simultaneously maximize reservoir hydropower output and minimize water shortage rates. China's Three Gorges Reservoir (TGR), situated over the upper reaches of the Yangtze River, constitutes the case study, with the standard operation policy (SOP) serving as a benchmark. Results showcase the efficacy of the proposed method, with substantial improvements observed: a 10.6% increase in hydropower output, a 6.0% reduction in water shortage days, and a 9.5% decrease in minimal reservoir water release compared to SOP. This study provides robust technical and scientific bolster to optimize reservoir ESC and enhance the synergy between hydropower generation, water supply, and drought resilience. Additionally, it offers decision-makers actionable strategies that account for emergency water supply capacities. These strategies aim to support mega reservoir's resilience against extreme drought events facilitating the collaboration between modelers and policy-makers, by means of intelligent optimization and decision-making technologies.

Optimal truss design with MOHO: A multi-objective optimization perspective

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO's solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

MOBCA: Multi-Objective Besiege and Conquer Algorithm

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution.

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO .

Bunch Ash biomass source for the synthesis of Al2(SiO4)2 magnetic nanocatalyst and as alkali catalyst for the synthesis of biodiesel production

This work employed the Admixture of oil from winter squash seed oil and duck waste fat for the synthesis of biodiesel using a derived heterogeneous catalyst from burnt Arecaceae kernel empty bunch (BAKEB). The admixture oil was obtained using the gravity ratio method and the properties of the oils were determined. The developed BAKEB was characterized using SEM, FTIR, XRF-FT, BET-adsorption, and qualitative analysis. Transesterification of the admixture oil to biodiesel was carried out in a single transesterification batch reactor, while Process optimization was carried out via RSM-CCD with four constraint variables namely: reaction period, catalyst conc., reaction temperature, and E-OH/OMR, respectively. The spent catalyst was recycled and reused and the quality of the produced biodiesel was compared with the recommended standard. Results showed the admixture oil ratio of 48:52 was sufficient to produce a validated optimum biodiesel yield of 99.42% (wt./wt.) at the reaction time of 55 min, catalyst conc. of 3.00 (%wt.), reaction temperature of 60 °C, and E-OH/OMR of 5.5:1 (vol./vol.), respectively. ANOVA analysis indicated that all variables were mutually significant at p-value<0.0001.The developed BAKEB was found to contain high percentages of Al-K-Na-Ca. The catalyst recyclability test indicated that BAKEB can be refined and reused. The produced biodiesel qualities have fuel properties similar to conventional diesel when compared with ASTM D6751 and EN 14,214. The study concluded that the blending of winter squash seed oil with duck waste fat in the ratio of 48:52 as feedstock for biodiesel synthesis is viable.

An improved multi-objective marine predator algorithm for gene selection in classification of cancer microarray data

Gene selection (GS) is an important branch of interest within the field of feature selection, which is widely used in cancer classification. It provides essential insights into the pathogenesis of cancer and enables a deeper understanding of cancer data. In cancer classification, GS is essentially a multi-objective optimization problem, which aims to simultaneously optimize the two objectives of classification accuracy and the size of the gene subset. The marine predator algorithm (MPA) has been successfully employed in practical applications, however, its random initialization can lead to blindness, which may adversely affect the convergence of the algorithm. Furthermore, the elite individuals in guiding evolution are randomly chosen from the Pareto solutions, which may degrade the good exploration performance of the population. To overcome these limitations, a multi-objective improved MPA with continuous mapping initialization and leader selection strategies is proposed. In this work, a new continuous mapping initialization with ReliefF overwhelms the defects with less information in late evolution. Moreover, an improved elite selection mechanism with Gaussian distribution guides the population to evolve towards a better Pareto front. Finally, an efficient mutation method is adopted to prevent evolutionary stagnation. To evaluate its effectiveness, the proposed algorithm was compared with 9 famous algorithms. The experimental results on 16 datasets demonstrate that the proposed algorithm can significantly reduce the data dimension and obtain the highest classification accuracy on most of high-dimension cancer microarray datasets.

MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p (Δ P ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

Multi-objective chaos game optimization

The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.

A Non-Dominated Sorting Based Multi-Objective Neural Network Algorithm

Neural Network Algorithm (NNA) is a recently proposed Metaheuristic that is inspired by the idea of artificial neural networks. The performance of NNA on single-objective optimization problems is very promising and effective. In this article, a maiden attempt is made to restructure NNA for its possible use to address multi-objective optimization problems. To make NNA suitable for MOPs several fundamental changes in the original NNA are proposed. A novel concept is proposed to initialize the candidate solution, position update, and selection of target solution. To examine the optimization ability of the proposed scheme, it is tested on several benchmark problems and the results are compared with eight state-of-the-art multi-objective optimization algorithms. Inverse generational distance(IGD) and hypervolume (HV) metrics are also calculated to understand the optimization ability of the proposed scheme. The results are statistically validated using Wilcoxon signed rank test. It is observed that the overall optimization ability of the proposed scheme to solve MOPs is very good.•This paper proposes a method to solve multi-objective optimization problems.•A multi-objective Neural Network Algorithm method is proposed.•The proposed method solves difficult multi-objective optimization problems.

Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up.•A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed.•Small to large-scale truss design challenges are proposed for the validation.•The performance is measured using four metrics and Friedman's rank test.

Chaos-enhanced multi-objective tunicate swarm algorithm for economic-emission load dispatch problem

Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions and fuel costs. Many approaches have demonstrated their efficacy in addressing CEED problem. However, designing a robust algorithm capable of achieving the Pareto optimal solutions under its multimodality and non-convexity natures caused by valve ripple effects is a true challenge. In this paper, chaos-enhanced multi-objective tunicate swarm algorithm (CMOTSA) for CEED problem. To promote the exploration and exploitation abilities of the basic tunicate swarm algorithm (TSA), an exponential strategy based on chaotic logistic map (ESCL) is incorporated. Based on ESCL in CMOTSA, it can improve the possibility of diversification feature to search different areas within the solution space, and then, gradually with the progress of iterative process it converts to emphasize the intensification ability. The efficacy of CMOTSA is approved by applying it to some of multi-objective benchmarking functions which have different Pareto front characteristics including convex, discrete, and non-convex. The inverted generational distance (IGD) and generational distance (GD) are employed to assess the robustness and the good quality of CMOTSA against some successful algorithms. Additionally, the computational time is evaluated, the CMOTSA consumes less time for most functions. The CMOTSA is applied to one of the practical engineering problems such as combined economic and emission dispatch (CEED) with including the valve ripples. By using three different systems (IEEE 30-bus with 6 generators system, 10 units system and IEEE 118-bus with 14 generating units), the methodology validation is made. It can be stated for the large-scale case of 118-bus systems that the results of the CMOTSA are equal to 8741.3 $/h for the minimum cost and 2747.6 ton/h for the minimum emission which are very viable to others. It can be pointed out that the cropped results of the proposed CMOTSA based methodology as an efficient tool for CEED is proven.

Machine learning approach for corona virus disease extrapolation: A case study

Supervised/unsupervised machine learning processes are a prevalent method in the field of Data Mining and Big Data. Corona Virus disease assessment using COVID-19 health data has recently exposed the potential application area for these methods. This study classifies significant propensities in a variety of monitored unsupervised machine learning of K-Means Cluster procedures and their function and use for disease performance assessment. In this, we proposed structural risk minimization means that a number of issues affect the classification efficiency that including changing training data as the characteristics of the input space, the natural environment, and the structure of the classification and the learning process. The three problems mentioned above improve the broad perspective of the trajectory cluster data prediction experimental coronavirus to control linear classification capability and to issue clues to each individual. K-Means Clustering is an effective way to calculate the built-in of coronavirus data. It is to separate unknown variables in the database for the disease detection process using a hyperplane. This virus can reduce the proposed programming model for K-means, map data with the help of hyperplane using a distance-based nearest neighbor classification by classifying subgroups of patient records into inputs. The linear regression and logistic regression for coronavirus data can provide valuation, and tracing the disease credentials is trial.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Modeling the leader–follower supply chain network under uncertainty and solving by the HGALO algorithm

The purpose of this article is to develop a competitive supply chain network (SCN) in the face of uncertainty. The objective of the leader chain is to maximize total network profits by strategically locating suppliers, manufacturers, distribution centers, and retailers. Additionally, the follower chain seeks to maximize the network's profit. Both factors, optimal flow allocation to different echelons of the SCN and product pricing, are examined in the leader chain and follower chain. The KKT conditions are used in this article to convert a bi-level model to a one-level model. Additionally, a fuzzy programming technique is used to control the problem's uncertain parameters. According to the results obtained using the fuzzy programming technique, increasing the uncertainty rate increases demand while decreasing the OBFV and average selling price of products. Finally, the problem was untangled using a novel hybrid genetic and ant-lion optimization algorithm (HGALO). The results of problem solving in larger sizes demonstrate HGALO's superior efficiency in comparison with the other algorithm.

A Grasshopper Optimization Algorithm-Based Response Surface Method for Non-Probabilistic Structural Reliability Analysis with an Implicit Performance Function

Non-probabilistic reliability analysis has great developmental potential in the field of structural reliability analysis, as it is often difficult to obtain enough samples to construct an accurate probability distribution function of random variables based on probabilistic theory. In practical engineering cases, the performance function (PF) is commonly implicit. Monte Carlo simulation (MCS) is commonly used for structural reliability analysis with implicit PFs. However, MCS requires the calculation of thousands of PF values. Such calculation could be time-consuming when the structural systems are complicated, and numerical analysis procedures such as the finite element method have to be adopted to obtain the PF values. To address this issue, this paper presents a grasshopper optimization algorithm-based response surface method (RSM). First, the method employs a quadratic polynomial to approximate the implicit PF with a small set of the actual values of the implicit PF. Second, the grasshopper optimization algorithm (GOA) is used to search for the global optimal solution of the scaling factor of the convex set since the problem of solving the reliability index is transformed into an unconstrained optimal problem. During the search process in the GOA, a dynamic response surface updating strategy is used to improve the approximate accuracy near the current optimal point to improve the computing efficiency. Two mathematical examples and two engineering structure examples that use the proposed method are given to verify its feasibility. The results compare favorably with those of MCS. The proposed method can be non-invasively combined with finite element analysis software to solve non-probabilistic reliability analysis problems of structures with implicit PF with high efficiency and high accuracy.

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations

Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm garden balsam optimization (CMGBO) is proposed to ensure the convergence of well-diversified Pareto regions. The performance of the used algorithm is validated on 12 test functions. The algorithm is employed to solve four real-world problems in engineering. The achieved consequences corroborate the advantage of the proposed algorithm with regard to convergence and diversity.

Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm

The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm.

A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.