A co-evolving framework for robust particle swarm optimization. (English) Zbl 1143.65046

Luo, Qiang; Yi, Dongyun

Appl. Math. Comput. 199, No. 2, 611-622 (2008).

Summary: Particle swarm optimization (PSO) as an efficient and powerful problem-solving strategy has been widely used, but appropriate adjustment of its parameters usually requires a lot of time and labor. So a co-evolving framework is proposed to improve the robustness of the PSO. Within this framework the fuzzy rules for the manipulation of the inertia weights are co-evolved with the particles. The simulation results on a suite of test functions show that the use of this co-evolving framework improves the performance of the PSO, especially the robustness against the dimensional variation of the test functions.

MSC:

65K05	Numerical mathematical programming methods
90C15	Stochastic programming

Keywords:

particle swarm optimization; evolutionary algorithm; numerical optimization; fuzzy logical controller; analysis of algorithms; numerical examples

References:

[2]	Shi, Y.; Eberhart, R. C., Empirical study of particle swarm optimization. Empirical study of particle swarm optimization, Proceedings of Congress on Evolutionary Computation (1999), IEEE Service Center: IEEE Service Center Piscataway, NJ
[3]	Ueno, G.; Yasuda, K.; Iwasaki, N., Robust adaptive particle swarm optimization, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 4, 3915-3920 (2005)
[6]	Mendes, R.; Kennedy, J.; Neves, J., The fully informed particle swarm: simpler, maybe better, IEEE Transaction on Evolutionary Computation, 8, 204-210 (2004)
[7]	Dong, Y.; Tang, J.; Xu, B.; Wang, D., An application of swarm optimization to nonlinear programming, Computers and Mathematics with Applications, 49, 11-12, 1655-1668 (2005) · Zbl 1127.90407
[8]	Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transaction on Evolutionary Computation, 10, 3, 281-295 (2006)
[9]	Shelokar, P. S.; Siarry, P.; Jayaraman, V. K.; Kulkarni, B. D., Particle swarm and ant colony algorithms hybridized for improved continuous optimization, Applied Mathematics and Computation, 188, 1, 129-142 (2007) · Zbl 1114.65334
[10]	Ioan, C. T., The particle swarm optimization algorithm convergence analysis and parameter selection, Information Processing Letters, 85, 317-325 (2003) · Zbl 1156.90463
[11]	Kadirkamanathan, V.; Selvarajah, K.; Fleming, P. J., Stability analysis of the particle dynamics in particle swarm optimizer, IEEE Transaction on Evolutionary Computation, 10, 3, 245-255 (2006)
[13]	Krasnogor, N.; Gustafson, S., A study on the use of “self-generation” in memetic algorithms, Natural Computing, 3, 1, 53-76 (2004) · Zbl 1074.68064