Grey-Based Particle Swarm Optimization Algorithm
In order to apply grey relational analysis to the evolutionary process, a modified grey relational analysis is introduced in this study. Then, with the help of such a grey relational analysis, this study also proposed a grey-based particle swarm optimization algorithm in which both inertia weight and acceleration coefficients are varying over the generations. In each generation, every particle has its own algorithm parameters and those parameters may differ for different particles. The proposed PSO algorithm is applied to solve the optimization problems of twelve test functions for illustration. Simulation results are compared with the other three variants of PSO to demonstrate the search performance of the proposed algorithm.
KeywordsAcceleration coefficients Grey relational analysis Inertia weight Particle swarm optimization
Unable to display preview. Download preview PDF.
- 1.Kennedy, J., Eberhart, R.C.: A new optimizer using particle swarm theory. In: Proc. 6th Intl. Symp. Micro Machine Human Sci., pp. 39–43. IEEE Press, New York (1995)Google Scholar
- 2.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. 1991 IEEE Neural Netw., pp. 1942–1948. IEEE Press, New York (1995)Google Scholar
- 10.Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. IEEE World Congr. Comput. Intell., pp. 69–73. IEEE Press, New York (1998)Google Scholar