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 si...
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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-objectiveevolutionary 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 chaoticevolution (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.
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