Scale factor local search in differential evolution

De Montfort University Open Research Archive

Show simple item record Neri, Ferrante en Tirronen, Ville en 2012-08-14T10:54:57Z 2012-08-14T10:54:57Z 2009-06
dc.identifier.citation Neri, F. and Tirronen, V. (2009) Scale factor local search in differential evolution. Memetic Computing Journal, 1, (2), pp. 153-171 en
dc.identifier.issn 1865-9284
dc.description.abstract This paper proposes the scale factor local search differential evolution (SFLSDE). The SFLSDE is a differential evolution (DE) based memetic algorithm which employs, within a self-adaptive scheme, two local search algorithms. These local search algorithms aim at detecting a value of the scale factor corresponding to an offspring with a high performance, while the generation is executed. The local search algorithms thus assist in the global search and generate offspring with high performance which are subsequently supposed to promote the generation of enhanced solutions within the evolutionary framework. Despite its simplicity, the proposed algorithm seems to have very good performance on various test problems. Numerical results are shown in order to justify the use of a double local search instead of a single search. In addition, the SFLSDE has been compared with a standard DE and three other modern DE based metaheuristic for a large and varied set of test problems. Numerical results are given for relatively low and high dimensional cases. A statistical analysis of the optimization results has been included in order to compare the results in terms of final solution detected and convergence speed. The efficiency of the proposed algorithm seems to be very high especially for large scale problems and complex fitness landscapes en
dc.language.iso en en
dc.publisher Springer en
dc.subject differential evolution en
dc.subject adaptive memetic algorithms en
dc.subject golden section search en
dc.subject multimeme algorithms en
dc.subject large scale optimization en
dc.title Scale factor local search in differential evolution en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en

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