Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:A3L-A

Session:

Number:162

The Fundamental Characteristic of Hysteresis-Divided Optimization

Masafumi Kubota,  Kenya Jin'no,  Toshimichi Saito,  

pp.162-165

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.162

PDF download (410.8KB)

In this paper, we propose a hysteresis divided optimization algorithm (HDO) which can be classified into one of meta-heuristic optimization algorithms. The searching area is divided into some region which corresponds to the number of particles. The algorithm consists with plural particles which search the optimal value of the given evaluation function. Each particle is placed in each region, and the particle searches the optimum value within the corresponding region. The size of each region is determined by the adjacent best informations. Also, each search region is discretized in order to reduce the computational amount of search process. The moving direction of the particle is determined by the output of the bipolar hysteresis. Namely, the particles continue to explore in each region. By using these properties of the HDO, we apply the HDO to the multi-solution problems (MSP). We confirm the search performance of the HDO by using well-known benchmark function of MSP. Based on the numerical simulation results, the HDO exhibits the good performance.

[1] A. P. Engelbrecht, ”Fundamentals of Computational Swarm Intelligence”, Willey, 2005.

[2] K. E. Parsopoulos and M. N. Vrahatis, ”On the computation of all global minimizers through particle swarm optimization,” IEEE Trans. Evol. Comput. , vol. 8, no. 3, pp. 211-224, 2004.

[3] D. Parrott and X. Li, ”Locating and tracking multiple dynamic optima by a particle swarm model using speciation,” IEEE Trans. Evol. Comput., 10, 4. pp. 440-458, 2006

[4] X. Li, ”Niching without niching parameters: particle swarm optimization using a ring topology,” IEEE Trans. Evol. Comput., 14, 1. pp. 150-169, 2010

[5] S. Yang and C. Li, ”A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments,” IEEE Trans. Evol. Comput., 14, 6. pp. 959-974, 2010

[6] M. Kubota and T. Saito, ”A Discrete Particle Swarm Optimizer for Multi-Solution Problems,” IEICE Trans. Fundamentals, E95-A, 1, pp. 406-409, 2012.

[7] R. Sano, T. Shindo, K. Jin'no and T. Saito, ”PSO-based Multiple Optima Search Systems with Switched Topology”, Proc. IEEE CEC, pp. 3302-3307, 2012.