Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia8403Research ArticleSelf-Adaptation in Swarm Optimization AlgorithmsPoluyanS VFaculty of Natural and Engineering Sciencesvpoluyan@gmail.comReinhardN MFaculty of Natural and Engineering Sciencenickreinhard@gmail.comErshovN MFaculty of Computational Mathematics and Cyberneticsershovnm@gmail.comDubna International University for Nature, Society and ManLomonosov Moscow State University15022014241541808092016Copyright © 2014,2014Evolutionary algorithms are in active development for last two decades, due to numerous studies in the field of mathematical biology, and the wide spread of massively parallel computing systems since numerical modeling of biological systems (with significant degree of parallelism) requires significant computations. Swarm optimization algorithms discussed in this article are based on modeling of collective behavior in large colonies of animals, such as ants, bacteria, bees. Such algorithms are universal and applicable to a wide range of computational problems. The present paper is devoted to the new approach to the construction of self adaptive swarm optimization algorithms, which automatically adjusts parameters of the algorithm in the process of its evolution. The idea of building self adaptive evolutionary algorithms is based on the using in the background to the main algorithm (e.g., bacterial foraging algorithm) auxiliary genetic algorithm, the purpose of which is to adjust the parameters of the basic algorithm, providing the highest possible rate of its convergence. The application of the proposed scheme of self-adaptation on the examples of bacterial foraging algorithm and bees algorithms is considered. The results of the numerical study of such algorithms on the standard test problems of continuous optimization, demonstrating the efficiency of the proposed scheme of self-adaptation, are described.swarm optimizationself-adaptationbacterial foraging algorithmbees algorithmgenetic algorithmроевая оптимизациясамоадаптациябактериальный алгоритмпчелиный поискгенетический алгоритм[Bonabeau E., Dorigo M., Theraulaz G. Swarm Intelligence: from Natural to Artificial Systems. - New York: Oxford University Press, Inc., 1999.][Passino K.M. Biomimicry of Bacterial Foraging for Distributed Optimization and Control // IEEE Control Systems Magazine. - 2002. - Vol. 22, No 3. - Pp. 52-67.][The Bees Algorithm, a Novel Tool for Complex Optimisation Problems / D.T. Pham, A. Ghanbarzadeh, E. Koc et al. // Proceedings of the 2nd International Virtual Conference on Intelligent Production Machines and Systems (IPROMS 2006). - 2006. - Pp. 454-459.][Benchmark Functions for the CEC’2010 Special Session and Competition on LargeScale Global Optimization: Techrep / K. Tang, X. Li, P. N. Suganthan et al. / University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL): Hefei, Anhui, China. - 2010.][Whitley D. A Genetic Algorithm Tutorial // Statistics and Computing. - 1994. - Vol. 4, No 2. - Pp. 65-85.]