A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part I-Past and Future
Ke Li

TL;DR
This survey comprehensively reviews the development, core components, and advanced topics of decomposition-based evolutionary multi-objective optimization, focusing on MOEA/D, and discusses future research directions.
Contribution
It systematically analyzes MOEA/D's evolution, design components, and emerging trends, providing a valuable resource for researchers in EMO.
Findings
Detailed history of MOEA/D development
Analysis of core design components
Overview of advanced topics and future directions
Abstract
Decomposition has been the mainstream approach in classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization (EMO) until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this two-part survey series, we use MOEA/D as the representative of decomposition-based EMO to review the up-to-date development in this area, and systematically and comprehensively analyze its research landscape. In the first part, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Evolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research
