Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm
The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distr...
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| Vydáno v: | IEEE transactions on cybernetics Ročník 55; číslo 3; s. 1224 - 1237 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.03.2025
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| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
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| Abstract | The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method. |
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| AbstractList | The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method.The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method. The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method. |
| Author | Pedrycz, Witold Wang, Hui Cai, Tie |
| Author_xml | – sequence: 1 givenname: Hui orcidid: 0000-0002-2594-3043 surname: Wang fullname: Wang, Hui email: hnndwh@sina.com organization: School of Computer Science and Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, China – sequence: 2 givenname: Tie surname: Cai fullname: Cai, Tie email: cait@sziit.edu.cn organization: School of Computer Science and Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, China – sequence: 3 givenname: Witold orcidid: 0000-0002-9335-9930 surname: Pedrycz fullname: Pedrycz, Witold email: wpedrycz@ualberta.ca organization: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada |
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| SubjectTerms | Bayes methods Constraint multiobjective particle swarm optimization (PSO) algorithm Costs Entropy Kriging model Kriging surrogate model-based local search of simplex crossover operator (KLSSCO) Mathematical models Optimization Particle swarm optimization Robustness Scalability Search problems Shape simple cross-over |
| Title | Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm |
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