A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems

Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained mu...

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Vydáno v:	Complex & intelligent systems Ročník 10; číslo 1; s. 655 - 675
Hlavní autoři:	Shi, Chenli, Wang, Ziqi, Jin, Xiaohang, Xu, Zhengguo, Wang, Zhangsheng, Shen, Peng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.02.2024 Springer Nature B.V Springer
Témata:	Coevolution Complexity Computational Intelligence Constrained multi-objective optimization problems (CMOPs) Constraints Convergence Data Structures and Information Theory Engineering Evolutionary algorithms Genetic algorithms Multiple objective analysis Optimization Original Article Parallel algorithm Pareto optimization Search process Staging strategy Constrained multi-objective optimization problems (CMOPs) Parallel algorithm Evolutionary algorithms Staging strategy Coevolution
ISSN:	2199-4536, 2198-6053
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Abstract	Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop , which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs.
AbstractList	Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop , which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs. Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop, which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs. Abstract Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop, which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs.
Author	Jin, Xiaohang Wang, Zhangsheng Shi, Chenli Wang, Ziqi Xu, Zhengguo Shen, Peng
Author_xml	– sequence: 1 givenname: Chenli surname: Shi fullname: Shi, Chenli organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University – sequence: 2 givenname: Ziqi surname: Wang fullname: Wang, Ziqi organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University – sequence: 3 givenname: Xiaohang surname: Jin fullname: Jin, Xiaohang organization: College of Mechanical Engineering, Zhejiang University of Technology, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology – sequence: 4 givenname: Zhengguo orcidid: 0000-0003-0599-7324 surname: Xu fullname: Xu, Zhengguo email: xzg@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University – sequence: 5 givenname: Zhangsheng surname: Wang fullname: Wang, Zhangsheng organization: School of Mechanical Engineering, Tongji University – sequence: 6 givenname: Peng surname: Shen fullname: Shen, Peng organization: Jurong Power Generation Branch, Huadian Jiangsu Energy Co., Ltd
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Snippet	Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective... Abstract Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained...
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SubjectTerms	Coevolution Complexity Computational Intelligence Constrained multi-objective optimization problems (CMOPs) Constraints Convergence Data Structures and Information Theory Engineering Evolutionary algorithms Genetic algorithms Multiple objective analysis Optimization Original Article Parallel algorithm Pareto optimization Search process Staging strategy
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Title	A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems
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