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Dynamic multi-objective evolutionary algorithms for single-objective optimization
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DOI：10.1016/j.asoc.2017.08.030.png)
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Abstract
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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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Evolutionary computation
Multi-objective optimization
Niching
Dynamic optimization
Antenna array
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