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An improved dynamic multi-objective robust evolutionary algorithm and application based on PSTL
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AI
DOI：10.1016/j.swevo.2025.102195.png)
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Abstract
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A problem whose optimal solution evolves as environmental parameters change is known as a dynamic multi-objective optimization problem (DMOP). Commonly used approaches to DMOP are generally grouped into two categories: Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) and the Dynamic Multi-Objective Robust Evolutionary Algorithm (DMOREA). DMOEA tracks the dynamic Pareto optimal solution through the dynamic response strategy, but it will lead to a high switching cost. DMOREA looks for robust solutions that is suitable in multiple environments, but the optimization effect is poor. To solve these problems, an improved dynamic multi-objective robust evolutionary algorithm based on preliminary search and transfer learning is proposed. Firstly, the preliminary search strategy is used to generate a high-quality target domain guiding population to avoid the occurrence of negative migration. Transfer learning is used to generate a well-distributed population and accelerate the convergence speed. Then, a switching strategy based on the severity of environmental change is proposed, which evaluates the applicability of DMOREA's robust solutions in future environments, switching between solutions generated by preliminary search and transfer learning or existing robust solutions. The proposed strategy improves the optimization effect of the algorithm while maintaining its robustness. The effectiveness of the proposed algorithm is verified by comparison with other algorithms.
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