A note on the learning automata based algorithms for adaptive parameter selection in PSO

: PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that p...

• Type: Journal
• Year: 2011
• Where: ASC
• Authors: Ali B. Hashemi, Mohammad Reza Meybodi
• Keywords: Artificial Intelligence, ASC 2011, Automata Based Algorithms, Parameter, Parameter Values