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A Constrained Multi-objective Differential Evolution Based on Hybrid Mutation Strategy
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DOI：10.1007/s13369-025-10149-2.png)
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Abstract
En 中文
There are two main challenges in using evolutionary algorithms to solve constrained multi-objective optimization problems. The first is balancing exploration and exploitation capabilities. To address this challenge, we propose a new search algorithm based on four mutation strategies with different characteristics in this study. Two mutation strategies enhance exploration ability, and the other two enhance exploitation ability. The second challenge is balancing the objective functions and constraints. To address this challenge, we propose a random selection of the best individual from two rankings when selecting parent individuals for the mutation operation and an improved constraint handling technique. The first ranking uses Pareto dominance and crowding distance, and the second uses the constrained dominance principle and crowding distance. Finally, we propose a constrained multi-objective differential evolution with hybrid mutation strategy (CMODEHMS). We evaluated it with 51 benchmark test functions and 21 real-world problems to analyze and compare CMODEHMS with eight state-of-the-art constrained multi-objective evolutionary algorithms. The experimental results demonstrated that the proposed CMODEHMS exhibited superior performance compared with competing methods.
Keywords:
Differential evolution
Constrained multi-objective optimization
Hybrid mutation strategy
Constraint handling technique
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