Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

• URL: http://arxiv.org/abs/2108.09588v1
• Date: Sat, 21 Aug 2021 22:21:44 GMT
• Title: Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities
• Authors: Ke Li
• Abstract summary: We present a survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. selected major developments of MOEA/D are reviewed according to its core design components. We shed some lights on emerging directions for future developments.
• Score: 5.760976250387322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decomposition has been the mainstream approach in the 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 until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, 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 MOEA/D are reviewed according to its core design components including weight vector settings, sub-problem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

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