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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Bibliographic Details
Published in:Complex & intelligent systems Vol. 10; no. 1; pp. 655 - 675
Main Authors: Shi, Chenli, Wang, Ziqi, Jin, Xiaohang, Xu, Zhengguo, Wang, Zhangsheng, Shen, Peng
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2024
Springer Nature B.V
Springer
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-023-01181-6