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

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 traditional methods, achieving superior optimization accuracy and robustness across a variety of benchmark functions.