A coevolution algorithm based on two-staged strategy for constrained multi-objective problems
Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimi...
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| Vydáno v: | Applied intelligence (Dordrecht, Netherlands) Ročník 52; číslo 15; s. 17954 - 17973 |
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| Jazyk: | angličtina |
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01.12.2022
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimization evolutionary algorithm (TSC-CMOEA) is presented to solve constrained multi-objective optimization problems. This method divides the search process into two phases: in the first stage, the synchronous co-evolution is used, and the population corresponding to the help problem and the population corresponding to the raw problem cooperate with each other and share the offspring to produce better solutions, so as to quickly cross the infeasible region and approach the Pareto front; The second stage discards the help problem when it fails and maintains only the evolution of the main population to save computing resources and enhance convergence. The combination of synchronous co-evolution and staged strategy allows the population to traverse infeasible regions more efficiently and converge quickly to feasible and non-dominant regions. The test results on benchmark CMOPs show that the convergence and population distribution of TSC-CMOEA is significantly better than those of NSGA-II, NSGA-III, C-MOEA/D, PPS, ToP and CCMO. |
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| AbstractList | Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimization evolutionary algorithm (TSC-CMOEA) is presented to solve constrained multi-objective optimization problems. This method divides the search process into two phases: in the first stage, the synchronous co-evolution is used, and the population corresponding to the help problem and the population corresponding to the raw problem cooperate with each other and share the offspring to produce better solutions, so as to quickly cross the infeasible region and approach the Pareto front; The second stage discards the help problem when it fails and maintains only the evolution of the main population to save computing resources and enhance convergence. The combination of synchronous co-evolution and staged strategy allows the population to traverse infeasible regions more efficiently and converge quickly to feasible and non-dominant regions. The test results on benchmark CMOPs show that the convergence and population distribution of TSC-CMOEA is significantly better than those of NSGA-II, NSGA-III, C-MOEA/D, PPS, ToP and CCMO. |
| Author | Cheng, Fanyong Zeng, Zhenhuan Fan, Chaodong Ai, Zhaoyang Wang, Jiawei Xiao, Leyi |
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| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_106004 crossref_primary_10_3390_math13091441 crossref_primary_10_1007_s40747_023_01181_6 crossref_primary_10_1007_s12293_024_00409_3 crossref_primary_10_1109_TEVC_2023_3345470 |
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| Keywords | Constrained multi-objective evolutionary algorithms Coevolution Multiobjective optimization Constraint handling technique |
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