Integrating Chaotic Evolutionary and Local Search Techniques in Decision Space for Enhanced Evolutionary Multi-Objective Optimization
Xiang Meng

TL;DR
This paper introduces novel chaotic evolutionary algorithms combined with local search and clustering techniques to improve multi-objective and single-objective optimization, demonstrating superior performance on benchmark problems.
Contribution
It develops new algorithms integrating chaotic dynamics with niching, clustering, and uncertainty-based selection for enhanced optimization in decision space.
Findings
Algorithms outperform traditional methods in accuracy and robustness.
Chaotic dynamics improve population diversity and search efficiency.
Proposed methods show superior results on benchmark functions.
Abstract
This paper presents innovative approaches to optimization problems, focusing on both Single-Objective Multi-Modal Optimization (SOMMOP) and Multi-Objective Optimization (MOO). In SOMMOP, we integrate chaotic evolution with niching techniques, as well as Persistence-Based Clustering combined with Gaussian mutation. The proposed algorithms, Chaotic Evolution with Deterministic Crowding (CEDC) and Chaotic Evolution with Clustering Algorithm (CECA), utilize chaotic dynamics to enhance population diversity and improve search efficiency. For MOO, we extend these methods into a comprehensive framework that incorporates Uncertainty-Based Selection, Adaptive Parameter Tuning, and introduces a radius \( R \) concept in deterministic crowding, which enables clearer and more precise separation of populations at peak points. Experimental results demonstrate that the proposed algorithms outperform…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms
