A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems

The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different direct...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 72825 - 72838
Main Author: Guo, Xiaofang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Convergence
Decomposition
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Many-objective
Multiple objective analysis
Optimization
Social factors
Statistics
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3188762