A Comprehensive Analysis on Enhancing Multi-Objective Evolutionary Algorithms Using Chaotic Dynamics and Dominant Relationship-Based Search Strategies

In optimization and decision-making, multi-objective optimization has emerged as a pivotal challenge. Over the past three decades, the concerted efforts of scholars and practitioners across various disciplines have significantly advanced the study and implementation of Multi-Objective Evolutionary A...

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Bibliographic Details
Published in:IEEE Access Vol. 13; pp. 33455 - 33470
Main Authors: Wang, Zitong, Pei, Yan, Li, Jianqiang
Format: Journal Article
Language:English
Published: IEEE 01.01.2025
Institute of Electrical and Electronics Engineers (IEEE)
Subjects:
Chaos
chaotic evolution
Evolutionary computation
Evolutionary multi-objective optimization
Heuristic algorithms
Linear programming
multi-objective chaotic evolution algorithm
multi-objective optimization problem
Optimization
Search problems
search strategy
Space exploration
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:In optimization and decision-making, multi-objective optimization has emerged as a pivotal challenge. Over the past three decades, the concerted efforts of scholars and practitioners across various disciplines have significantly advanced the study and implementation of Multi-Objective Evolutionary Algorithms (MOEAs). MOEAs stand at the forefront of multi-objective decision-making methodologies, marking a vibrant area of inquiry within evolutionary computation. This body of work categorizes MOEAs into three distinct streams: Decomposition-based MOEA algorithms, Dominant relationship-based MOEA algorithms, and Evaluation index-based MOEA algorithms. Focusing specifically on dominance-based MOEAs, this study integrates them with chaotic evolution (CE) strategies to enhance the efficacy of multi-objective optimization processes. Through comparative analysis against traditional algorithms, the newly proposed chaotic MOEA demonstrates superior optimization performance, thereby setting a robust groundwork for the continuous evolution and application of MOEAs.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3430970