- Issue
- Journal of Siberian Federal University. Mathematics & Physics. 2016 9 (2)
- Authors
- Sopov, Evgenii A.
- Contact information
- Sopov, Evgenii A.:Informatics and Telecommunications Institute Siberian State Aerospace University Krasnoyarsky Rabochy, 31, Krasnoyarsk, 660037 Russia;
- Keywords
- multimodal optimization; self-configuration; genetic algorithm; metaheuristic; niching
- 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
- Pages
- 246–257
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/20249
Journal of Siberian Federal University. Mathematics & Physics / Multiple Optima Identification Using Multi-strategy Multimodal Genetic Algorithm
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