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 MULTI-STRATEGY GENETIC ALGORITHM FOR NON-STATIONARY ENVIRONMENTS
E. A. Sopov
Siberian State Aerospace University named after academician M. F. Reshetnev 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660014, Russian Federation E-mail: evgenysopov@gmail.com
Many real-world problems of design and control in a field of the aerospace lead to optimization problems. Such optimization problems are complicated and become a great challenge to many optimization techniques. Moreover, many real-world optimization problems are dynamic and changing over time. Changes occur in the parameters, objectives and/or problem constraints. In this case, search algorithms should have the capability to track moving optima and adapt to a new environment. In past years many approaches for non-stationary optimization were proposed. The best results are achieved using a stochastic population-based search such as evolutionary and genetic algorithms. Unfortunately, real-world non-stationary optimization problems include various types of changes and are poorly predictable, thus there is a problem of choosing a proper optimization technique and tuning its parameters. This study presents a novel approach for designing a multi-strategy genetic algorithm based on a hybrid of the island model, cooperative and competitive coevolution schemes. The approach controls interactions of different genetic algorithms and leads to the self-configuring solving of problems with a priori unknown structure. A short survey on non-stationary optimization problem and methods is presented. The results of numerical experiments for benchmark problems from the CEC competition are discussed. The proposed approach has demonstrated efficiency comparable with other well-studied techniques for non-stationary optimization. And it has significant advantage – it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.
dynamic optimization, non-stationary environment, self-configuring, genetic algorithm, coevolution.
References
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  8. Ivanov I., Sopov E. [Investigation of the self-configured coevolutionary algorithm for complex multi-objective optimization problem solving]. Sistemy Upravleniya i Informatsionnye Tekhnologii. 2013, no. 1.1(51), p. 141–146 (In Russ.).
  9. Ivanov I., Sopov E. Design Efficient Technologies for Context Image Analysis in Dialog HCI Using Self-Configuring Novelty Search Genetic Algorithm. In the 11th International Conference on Informatics in Control, Automation and Robotics, ICINCO’14. Vienna, Austria, 2014, p. 832–839.
  10. Sopov E. A., Sopov S. A. The convergence prediction method for genetic and PBIL-like algorithms with binary representation. Proceeding of IEEE International Siberian Conference on Control and Communications (SIBCON’11), Tomsk, 2011, p. 203–206.
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Sopov Evgenii Alexandrovich – Cand. Sc., Docent, Docent of System Analysis and Operations Research Department, Siberian State Aerospace University named after academician M. F. Reshetnev. Е-mail: evgenysopov@gmail.com