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A Comprehensive Analysis on Enhancing Multi-Objective Evolutionary Algorithms Using Chaotic Dynamics and Dominant Relationship-Based Search Strategies
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DOI：10.1109/ACCESS.2024.3430970.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Heuristic algorithms
Chaos
Evolutionary computation
Search problems
Space exploration
Linear programming
Evolutionary multi-objective optimization
multi-objective optimization problem
chaotic evolution
multi-objective chaotic evolution algorithm
search strategy
optimization
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W