An Adaptive Staged PSO Based on Particles’ Search Capabilities
This study proposes an adaptive staged particle swarm optimization (ASPSO) algorithm based on analyses of particles’ search capabilities. First, the search processes of the standard PSO (SPSO) and the linear decreasing inertia weight PSO (LDWPSO) are analyzed based on our previous definition of exploitation. Second, three stages of the search process in PSO are defined. Each stage has its own search preference, which is represented by the exploitation capability of swarm. Third, the mapping between inertia weight, learning factor (w-c) and the exploitation capability is given. At last, the ASPSO is proposed. By setting different values of w-c in three stages, one can make swarm search the space with particular strategy in each stage, and the particles can be directed to find the solution more effectively. The experimental results show that the proposed ASPSO has better performance than SPSO and LDWPSO on most of test functions.
KeywordsParticle Swarm Optimization Exploitation and Exploration Staged Search Strategy Performances
Unable to display preview. Download preview PDF.
- 1.Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 2.Poli, R.: Analysis of the publications on the applications of particle swarm optimization. Journal of Artificial Evolution and Applications 2008, Article No. 4 (2008)Google Scholar
- 5.Feng, Y., Teng, G.F., Wang, A.X., Yao, Y.M.: Chaotic Inertia Weight in Particle Swarm Optimization. In: Second International Conference on Innovative Computing, Information and Control, pp. 475–478 (2007)Google Scholar
- 6.Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proc. of the IEEE Swarm Intelligence Symposlum(SIS), pp. 120–127 (2007)Google Scholar
- 8.Zhang, J.Q., Liu, K., Tan, Y., He, X.G.: Allocation of Local and Global Search Capabilities of Particle in Canonical PSO. In: GECCO 2008, Atlanta, Georgia, USA, pp. 165–166 (2008)Google Scholar
- 9.Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization (2005), http://www.ntu.edu.sg/home/EPNSugan