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Title: Differential evolution with neighborhood mutation for multimodal optimization
Authors: Qu, B. Y.
Suganthan, P. N.
Liang, J. J.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2012
Source: Qu, B. Y., Suganthan, P. N., & Liang, J. J. (2012). Differential evolution with neighborhood mutation for multimodal optimization. IEEE transactions on evolutionary computation, 16(5), 601-614.
Series/Report no.: IEEE transactions on evolutionary computation
Abstract: In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation is performed within each Euclidean neighborhood. The neighborhood mutation is able to maintain the multiple optima found during the evolution and evolve toward the respective global/local optimum. To test the performance of the proposed neighborhood mutation DE, a total of 29 problem instances are used. The proposed algorithms are compared with a number of state-of-the-art multimodal optimization approaches and the experimental results suggest that although the idea of neighborhood mutation is simple, it is able to provide better and more consistent performance over the state-of-the-art multimodal algorithms. In addition, a comparative survey on niching algorithms and their applications are also presented.
DOI: 10.1109/TEVC.2011.2161873
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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