Mathematics & Physics 2016, 9(2), 246–257 УДК 591.87 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 Received 11.01.2016, received in revised form 25.02.2016, accepted 22.03.2016 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. <...> DOI: 10.17516/1997-1397-2016-9-2-246-257 Introduction Many real-world problems have more than one optimal solution, or there exists only one global optimum and several local optima in the feasible solution space. <...> Evolutionary and genetic algorithms (EAs and GAs) demonstrate good performance for many complex optimization problems. <...> EAs and GAs are also efficient in the multimodal environment as they use a stochastic population-based search instead of the individual search in conventional algorithms. <...> At the same time, traditional EAs and GAs have a tendency to converge to the best-found optimum losing population diversity. <...> In recent years MMO have become more popular, and many efficient nature-inspired MMO techniques were proposed. <...> Almost all search algorithms are based on maintaining the population diversity, but differ in how the search space is explored and how optima basins are located and identified over a landscape. <...> The main reason is the better understanding of landscape features in the continuous search space. <...> Unfortunately many real-world MMO problems are usually considered as black-box optimization problems and are still a challenge forMMOtechniques. <...> Moreover, many real-world problems ∗evgenysopov@gmail.com ⃝ Siberian Federal <...>