Научная электронная библиотека ULRICH'S Periodicals Directory

Сибирский государственный университет
науки и технологий имени академика М.Ф. Решетнева

Сибирский аэрокосмический журнал
ISSN 2712-8970 (Print) ISSN 2782-5760 (on-line)

Сведения об образовательной организации

UDK 519.85
SELF-CONFIGURING ENSEMBLE OF GENETIC ALGORITHMS FOR MULTIMODAL OPTIMIZATION PROBLEMS
E. A. Sopov*, S. S. Aplesnin
Reshetnev Siberian State Aerospace University 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660037, Russian Federation *E-mail: evgenysopov@gmail.com
Multimodal optimization (MMO) is the problem of finding a set of all global and local optima or a good approxima-tion of that set. In recent years many efficient nature-inspired and evolutionary techniques (based on ES, PSO, DE and others) have been proposed for real-valued problems. At the same time, many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. 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 tech-niques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure (“black-box” optimization). The results of numerical experiments and of comparison with other popular techniques for classical benchmark problems and benchmark problems from the CEC’2013 competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to modern advanced algorithms. The main feature and advantage 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.
References

References

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Sopov Evgenii Aleksandrovich – Cand. Sc., Docent of Systems Analysis and Operations Research department, Reshetnev Siberian State Aerospace University. E-mail: evgenysopov@gmail.com.

Aplesnin Sergei Stepanovich – Dr. Sc., head of Physics department, Reshetnev Siberian State Aerospace University. E-mail: aplesnin@sibsau.ru.