Particle swarm optimization (PSO) algorithm simulates social behavior among individuals (or particles) "flying" through multidimensional search space. For enhancing the local search ability of PSO and guiding the search, a region that had most number of the particles was defined and analyzed in detail. Inspired by the ecological behavior, we presented a PSO algorithm with intermediate disturbance searching strategy (IDPSO), which enhances the global search ability of particles and increases their convergence rates. The experimental results on comparing the IDPSO to ten known PSO variants on 16 benchmark problems demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the IDPSO algorithm to multilevel image segmentation problem for shortening the computational time. Experimental results of the new algorithm on a variety of images showed that it can effectively segment an image faster. © 2013 Elsevier Inc. All rights reserved.
Bibliographical noteThe authors acknowledge support from City University of Hong Kong Strategic Research Grant (No. 7002826), the Introduction Foundation for the Talent of Nanjing University of Tele. and Com. (No. NY212025), National Natural Science Foundation of China (No. 61203270).
- Image segmentation
- Intermediate disturbance strategy
- Monte Carlo method
- Partial derivative theory
- Particle swarm optimization