Journal
INFORMATION SCIENCES
Volume 367, Issue -, Pages 719-746Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.05.049
Keywords
Multimodal optimisation; Memetic algorithm; Niching strategy
Categories
Funding
- Spanish Ministry of Education and Science [TIN2012-37930-C02-01, TIN2014-57251-P, TIN2013-47210-P, P10-TIC-6858, P12-TIC-2958]
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In this paper we propose a specially designed memetic algorithm for multimodal optimisation problems. The proposal uses a niching strategy, called region-based niching strategy, that divides the search space in predefined and indexable hypercubes with decreasing size, called regions. This niching technique allows our proposal to keep high diversity in the population, and to keep the most promising regions in an external archive. The most promising solutions are improved with a local search method and also stored in the archive. The archive is used as an index to effiently prevent further exploration of these areas with the evolutionary algorithm. The resulting algorithm, called Region-based Memetic Algorithm with Archive, is tested on the benchmark proposed in the special session and competition on niching methods for multimodal function optimisation of the Congress on Evolutionary Computation in 2013. The results obtained show that the region-based niching strategy is more efficient than the classical niching strategy called clearing and that the use of the archive as restrictive index significantly improves the exploration efficiency of the algorithm. The proposal achieves better exploration and accuracy than other existing techniques. (C) 2016 Elsevier Inc. All rights reserved.
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