Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathema...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 20; no. 3; pp. 475 - 480
Main Authors: Wang, Luping, Zhang, Qingfu, Zhou, Aimin, Gong, Maoguo, Jiao, Licheng
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
constraint
Constraints
Convergence
De-composition approach
Decomposition
Evolutionary algorithms
Evolutionary computation
Evolutionary multiobjective optimization
Linear programming
Measurement
Optimization
Scalars
Sociology
Statistics
Strategy
Constraint
evolutionary multiobjective optimization
decomposition approach
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2457616