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Region-based memetic algorithm with archive for multimodal optimsation.

Lacroix, Benjamin; Molina, Daniel; Herrera, Francisco


Daniel Molina

Francisco Herrera


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.

Journal Article Type Article
Publication Date Nov 1, 2016
Journal Information sciences
Print ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 367-368
Pages 719-746
Institution Citation LACROIX, B., MOLINA, D. and HERRERA, F. 2016. Region-based memetic algorithm with archive for multimodal optimsation. Information sciences [online], 367-368, pages 719-746. Available from:
Keywords Multimodal optimisation; Memetic algorithm; Niching strategy


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