Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 44, Issue 8, Pages 1314-1327Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2013.2282491
Keywords
Clustering method; differential evolution; multipopulation; niching; self-adaptive strategy
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Funding
- National Nature Science Foundation of China [61373174]
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Multimodal optimization is one of the most challenging tasks for optimization. It requires an algorithm to effectively locate multiple global and local optima, not just single optimum as in a single objective global optimization problem. To address this objective, this paper first investigates a cluster-based differential evolution (DE) for multimodal optimization problems. The clustering partition is used to divide the whole population into subpopulations so that different subpopulations can locate different optima. Furthermore, the self-adaptive parameter control is employed to enhance the search ability of DE. In this paper, the proposed multipopulation strategy and the self-adaptive parameter control technique are applied to two versions of DE, crowding DE (CDE) and species-based DE (SDE), which yield self-CCDE and self-CSDE, respectively. The new algorithms are tested on two different sets of benchmark functions and are compared with several state-of-the-art designs. The experiment results demonstrate the effectiveness and efficiency of the proposed multipopulation strategy and the self-adaptive parameter control technique. The proposed algorithms consistently rank top among all the competing state-of-the-art algorithms.
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