Enhancing Niching Method: A Learning Automata Based Approach Mohsen Jahanshahi 1+ , Mohammad Sadegh Kordafshari 2 , Majid Gholipour 2 1 Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran 2 Department of Computer Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran Abstract. In some problems of mathematics there are many functions which have several optimum points which all, should be computed. The method NichePSO is previously designed to accomplish this. This paper presents a new memetic-based scheme to enhance the NichePSO. This work utilizes a powerful soft computing tool namely Learning Automata as the local search algorithm in our proposed memetic approach. Numerical results demonstrate the superiority of the proposed method over the original NichePSO in terms of convergence and diversity. Keywords: PSO, Niching, Learning automata 1. Introduction Optimization is the minimization or maximization of an objective function that normally is done with consideration of limitations identifying conditions of a problem. In other words, it means the finding of the best solution for a given problem. Some problems have several local optimums which may be computed using Niching method. In this paper, memetic method is used for increase of convergence speed and also the increasing rate of particles’ diversity in Niching method, as two assessment factors of search methods. In this research, learning automata is used as a local search algorithm in memetic method. The rest of the paper is organized as follow: in section 2, PSO method is introduced. In section 3, NichePSO is briefly discussed. Section 4 explains the local search methods which are used and then proposed methods are discussed in section 5. The results of simulations are included in section 6. Section 7 is conclusion. 2. Particle Swarm Optimization Particle Swarm Optimization (PSO) was discussed in [1][2][3][4] and then has been improved for many years (e. g. [5][6]). It can be also used for several applications such as numerical optimization. Its main idea had been taken from Birds or fishes swarm behaviour which are searching for meal. Some birds are searching for meal randomly. Only a piece of meal may be found in the mentioned space. None of them knows about the real place of the meal. One of the best strategies is to follow a bird that is placed in minimum distance to a meal. In fact, this strategy is basis of PSO algorithm. This method is an effective technique for solution of optimization problems based on a swarm behaviour. In this method, each member of a swarm is called a particle who attempts to achieve a final solution with adjustment of its route and movement to the best personal and swarm experiences. In PSO algorithm, a solution is called a particle the + Corresponding author. E-mail address: mjahanshahi@iauctb.ac.ir 2011 International Conference on Computer and Software Modeling IPCSIT vol.14 (2011) © (2011) IACSIT Press, Singapore 174