Abstract-Numerous variations of Particle SwarmOptimization (PSO) algorithms have been recently developed, with the best aim of escaping from local minima. One of these recent variations is PSO-LA model which employs a Learning Automata (LA) that controls the velocity of the particle. Another variation of PSO enables particles to dynamically search through global and local space. This paper presents a Dynamic Global and Local Combined Particle Swarm Optimization based on a 3-action Learning Automata (DPSOLA). The embedded learning automaton accumulates the information from individuals, local best and global best particles then combines them to navigate the particle through the problem space. The proposed algorithm has been tested on eight benchmark functions with different dimensions. The work is unique from its test bed; evaluations contain large population size (150) and high dimension (150). The results show that, fitness and convergence pace is better than traditional PSO, DGLCPSO and previous PSO based LA algorithms.
I.INTRODUCTION One of the common methods for optimization of continuous nonlinear functions is particle swarm optimization (PSO) [1], [2]. This algorithm has two main concepts: artificial life and collective intelligent of brutes. The inertia weight (w) [3][4][5] is one of the PSO parameters to bring about a balance between the exploration and exploitation characteristics of PSO. Cooperative PSO (CPSO) is a variation on standard PSO which used multiple swarms [6]. Recently it is proposed a combined dynamic global and local particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO [7].Learning automaton (LA) [8] is a multi-propose casual tool, which is an expansion model for learning machines. PSO, like other stochastic search methods, is highly sensitive to adjustment of affective parameters. Recently a LA based PSO model [9][10][11][12] called PSO-LA has been reported to improve the performance of PSO. In [10] a PSO-LA model is proposed in which the LA is responsible for configuring the behavior of the swarm and also balancing the process of global and local search. In [11] a Cellular LA (CLA) based discrete PSO is introduced as a solution of premature convergence. To reduce the probability of trapping PSO-LA into local minima, in [12] four modifications on PSO-LA model are