This article is cited in 2 papers

Multiple optima identification using multi-strategy multimodal genetic algorithm

Evgenii A. Sopov

Informatics and Telecommunications Institute, Siberian State Aerospace University, Krasnoyarsky Rabochy, 31, Krasnoyarsk, 660037, Russia

Abstract: Multimodal optimization (MMO) is the problem of finding many or all global and local optima. In this study, a novel approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for classical benchmark problems and benchmark problems from the IEEE CEC competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to advanced algorithms. The main feature of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

Keywords: multimodal optimization, self-configuration, genetic algorithm, metaheuristic, niching.

UDC: 591.87

Received: 11.01.2016
Received in revised form: 25.02.2016
Accepted: 22.03.2016

Language: English

DOI: 10.17516/1997-1397-2016-9-2-246-257

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