Dynamic multi-objective evolutionary algorithms for single-objective optimization

•Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius pr...

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Published in:	Applied soft computing Vol. 61; pp. 793 - 805
Main Authors:	Jiao, Ruwang, Zeng, Sanyou, Alkasassbeh, Jawdat S., Li, Changhe
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2017
Subjects:	Antenna array Dynamic optimization Evolutionary computation Multi-objective optimization Niching Multi-objective optimization Niching Antenna array Evolutionary computation Dynamic optimization
ISSN:	1568-4946, 1872-9681
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Abstract	•Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius provides a proper balance between the exploitation and the exploration by gradually pushing the niche radius to zero. This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems.
AbstractList	•Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius provides a proper balance between the exploitation and the exploration by gradually pushing the niche radius to zero. This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems.
Author	Jiao, Ruwang Li, Changhe Zeng, Sanyou Alkasassbeh, Jawdat S.
Author_xml	– sequence: 1 givenname: Ruwang surname: Jiao fullname: Jiao, Ruwang organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: Sanyou surname: Zeng fullname: Zeng, Sanyou email: sanyouzeng@gmail.com organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China – sequence: 3 givenname: Jawdat S. orcidid: 0000-0003-4573-1328 surname: Alkasassbeh fullname: Alkasassbeh, Jawdat S. organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China – sequence: 4 givenname: Changhe orcidid: 0000-0001-9222-0702 surname: Li fullname: Li, Changhe email: lichanghe@cug.edu.cn organization: School of Automation, China University of Geosciences, Wuhan 430074, China
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