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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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
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Abstract	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.
AbstractList	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.
Author	Maoguo Gong Aimin Zhou Licheng Jiao Luping Wang Qingfu Zhang
Author_xml	– sequence: 1 givenname: Luping surname: Wang fullname: Wang, Luping – sequence: 2 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu – sequence: 3 givenname: Aimin surname: Zhou fullname: Zhou, Aimin – sequence: 4 givenname: Maoguo surname: Gong fullname: Gong, Maoguo – sequence: 5 givenname: Licheng surname: Jiao fullname: Jiao, Licheng
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SubjectTerms	Algorithms constraint Constraints Convergence De-composition approach Decomposition Evolutionary algorithms Evolutionary computation Evolutionary multiobjective optimization Linear programming Measurement Optimization Scalars Sociology Statistics Strategy
Title	Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm
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