Crossed Particle Swarm Optimization Algorithm
The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.
KeywordsSwarm Intelligence Benchmark Function Integral Control Contractive Factor Base Particle Swarm Optimization
Unable to display preview. Download preview PDF.
- 3.Shi, Y.H., Eberhat, R.C.: A Modified Particle Swarm Optimization. In: Proceedings of IEEE International Congress on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
- 4.Thanmaya, P., Kalyan, V., Chilukuri, K.M.: Fitness-Distance-Ratio Based Particle Swarm Optimization. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 174–181 (2003)Google Scholar