P System Based Particle Swarm Optimization Algorithm
Particle Swarm Optimization algorithm is a kind of excellent optimization algorithm, and has been widely used in many fields. In order to overcome the premature convergence and improve the accuracy of the PSO, we combine some related theories of membrane computing with PSO. The new algorithm can effectively balance the global search and partial optimization. Simulation results based on three bench functions show that the new algorithm can effectively solve the problem of premature, and effectively improve the convergence precision. At the same time, the algorithm in solving TSP problem also shows good optimization ability.
KeywordsMembrane Computing Particle swarm optimization Global search Partial optimization
This work is supported partially by National Science Fund of China (NO. 61170038), Science Fund of Shandong province (NO. ZR2011FM001), Social Science Fund of Shandong province (NO. 11CGLJ22).
- 1.Kennedy J, Eberhart R (1995) In: Particle swarm optimization: proceedings of IEEE international conference on neural networks, 1995[C]. Perth, Australia, IEEE, 1942–1948Google Scholar
- 2.Duan XD, Gao HX, Zhang XD (2007) Relations between population structure and population diversity of particle swarm optimization algorithm. Comput Sci 34(11):164–167Google Scholar
- 5.Yao K, Li FF, Liu XY (2007) Multi-particle swarm co-evolution algorithm. Comput Eng Appl 43(6):62–64Google Scholar
- 6.Luan LJ, Tan LJ, Niu B (2007) A novel hybrid global optimization algorithm based on particle swarm optimization and differential evolution. Inf Control 36(6):708–714Google Scholar
- 7.Paun G, Rozenberg G, Salomaa A (2009) Handbook of membrane computing. Oxford University Press, Oxford, pp 35–40Google Scholar
- 11.Riget J, Vesterstrom JS (2002) A diversity-guided particle swarm optimizer-the ARPSO. J RIGET, AarhusGoogle Scholar
- 12.Li L, Niu B (2009) Particle swarm optimization algorithm, Metallurgical Industry Press, Beijing, pp 44–51Google Scholar