Strategic Evolution: Adaptive Peak Replacement and Fused Response Techniques for Dynamic Constrained Multimodal Optimization

doi:10.1016/j.neucom.2025.131267

Strategic Evolution: Adaptive Peak Replacement and Fused Response Techniques for Dynamic Constrained Multimodal Optimization

2025-08-20

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DOI：10.1016/j.neucom.2025.131267

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This paper addresses the challenges posed by Dynamic Constrained Multimodal Problems (DCMMOPs), characterized by the integration of constraints, dynamics, and multimodality. We introduce an innovative adaptive neighborhood strategy known as Dynamic Neighborhood Adaptive Peak Replacement (DNAPR) to enhance the local search capability of the population. DNAPR strategically generates individuals in proximity to the current optimal solution and integrates dominant individuals to form the next generation. Additionally, we employ an adaptive strategy based on the mean difference between superior individuals of parent and offspring generations to prevent local optimum issues. Extending the multi-population concept, we enhance the traditional Memory-based Immigrants (MI) dynamic response strategy, introducing a Maximum Extension Distance (MED) to preserve population diversity amidst environmental changes. Finally, we propose a new algorithm framework, DMMCSA-DNAPR, integrating DNAPR with the Dynamic Multimodal Clonal Selection Algorithm (DMMCSA). Experimental results demonstrate the superior performance of DMMCSA-DNAPR across various accuracies and problem instances, establishing its efficacy in solving DCMMOPs.

Keywords:

Dynamic Constrained Multimodal Problems

Adaptive Neighborhood Strategy

Local Search Enhancement

Population Diversity

Clonal Selection Algorithm

Strategic Evolution: Adaptive Peak Replacement and Fused Response Techniques for Dynamic Constrained Multimodal Optimization

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