PARTICLE SWARM OPTIMIZATION WITH ADAPTIVE SOCIAL AND COGNITIVE COMPONENTS

Efficiency of solution finding by particle swarm optimization depends significantly on specific values of social and cognitive components used by a researcher. There is no known way currently to determine whether specific values of the components would provide maximal search efficiency in a particular case, or not. In order to eliminate this flaw, this article provides a modification of particle swarm optimization with adaptive social and cognitive components, which allows to fit particles movement to a particular problem during optimization process, thus removing the need of adjusting components manually. This adaption is based on genetic algorithms principles: it starts with a selection of the best performing particles, then crossover of their social and cognitive components with other particles, then mutation to provide some fluctuations of components. To evaluate algorithm’s performance a series of experiments on minimizing few test functions has been made. Minimums found by adaptive and canonical algorithms were averaged out and compared. Based on results, a statistical hypothesis that adaptive algorithm has better performance than canonical algorithm was confirmed. Provided research proves efficiency of adaptive particle swarm.

1. A.P. Karpenko. Overview of particle swarm optimizaion methods for global optimization / A.P. Karpenko, E.Y. Seliverstov // Mechanical engineering and computer technologies. – 2009 – №3 – 26 pp.

2. Jing-Ru Zhang. Hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training / Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R. Lyu // Applied Mathematics and Computation. – 2007 – №2 – pp. 1026-1037.

3. V. Miranda. EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems / V. Miranda, N. Fonseca // Transmission and Distribution Conference and Exhibition. – 2002 – №2 – pp. 740-750.

4. Chia-Feng Juang. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design / Chia-Feng Juang // Transactions on systems, man, and cybernetics – Part B: Cybernetics. – 2004 – №2 – pp. 997 – 1006.

5. T. Krink. The LifeCycle model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers» / T. Krink, M. Lovbjerg // Parallel Problem Solving from Nature — PPSN VII. – 2002 – pp. 621-630.

6. J. Kennedy. Particle swarm optimization / J. Kennedy, R. C. Eberhart // Proceedings of the 1995 IEEE International Conference on Neural Networks. – 1995 – pp. 1942-1948.

7. Shi Y. A modified particle swarm optimizer / Shi Y., Eberhart R. // Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. – 1998 – pp. 69-73.

8. Ratnaweera, A. «Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients» / Ratnaweera, A., Halgamuge, S., Watson, H. //IEEE Transactions on Evolutionary Computation. – 2004 – pp. 240-255.

9. J.H. Holland. Adaptation in natural and artificial systems / J.H. Holland. – Cambridge: MIT Press, 1992. – 225 pp.

10. M.V. Burakov. Genetic algorithm: theory and practice. / M.V. Burakov. – Saint-Petersburg: SUAI, 2008. – 164 pp.

11. Z. Michalewicz. A Note on Usefulness of Geometrical Crossover for Numerical Optimization Problems» / Z. Michalewicz, G. Nazhiyath, M. Michalewicz // Proceedings of the 5th Annual Conference on Evolutionary Programming. – 1996 – pp. 305-312.