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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Bibliographic Details
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
Online Access:Get full text
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Summary:•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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.08.030