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A constrained multi-objective evolutionary algorithm based on fitness landscape indicator
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AI
DOI：10.1016/j.asoc.2024.112128.png)
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Abstract
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Constrained multi-objective optimization problems (CMOPs) with constraints in both the decision and objective space are shown to be great challenges to be solved. Considering the different requirements of different problems on resource allocation of exploration and exploitation, this paper proposes a new constrained multi- objective evolutionary algorithm based on a novel fitness landscape indicator. The indicator regards the fitness landscape and evolutionary generation among the population to determine the selection of the offspring generation mechanism. The proposed algorithm uses the new indicator to select different differential evolutions during the evolutionary process to balance exploration and exploitation. Numerical experiments on three test suites and three practical examples compared with six existing algorithms show the proposed algorithm can effectively deal with different types of CMOPs, especially in CMOPs with constraints in both the decision and objective spaces.
Keywords:
Fitness landscape
Differential evolution
Fitness distance correlation
Evolutionary algorithm
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