A Novel Particle Swarm Optimization Algorithm

A Novel Particle Swarm Optimization Algorithm Shahriar Asta1 and A. Şima Uyar1, 1 Computer & Informatics Faculty Istanbul Technical University, Istanbul, Turkey {asta, etaner}@itu.edu.tr Abstract. In this study a novel memory based particle swarm optimization algorithm is presented. This algorithm utilizes external memory. A set of globally found best and worst positions, along with their parameters are stored in two separate external memories. At each iteration, a coefficient, based on the distance of the current particle to the closest best

and closest worst particles is calculated. When updating the velocity component, this coefficient is added to the current velocity of the particle with a certain probability. Also randomized upper and lower bound values have been defined for the inertia component. The algorithm is tested on benchmark functions and it is shown empirically that it converges faster to the optima. It also outperforms the PSO and a recent improved PSO, as well as maintaining a superior precision in comparison. Convergence speed is particularly important since the method will be used in a realistic robot motion simulator environment in which the simulation time is long enough to make convergence speed a primary concern. Keywords: Particle Swarm Optimization, External Memory. 1 Introduction Particle Swarm Optimization (PSO) is a nature inspired meta-heuristic method. This method was first introduced by Kennedy and Eberhart in 1995 [1]. It is inspired by the swarm behavior of birds flocking, and utilizes this behavior to guide the particles to search for globally optimal solutions. Basically, in PSO, a population of particles is spread randomly throughout the search space. The particles are assumed to be flying in the search space. The velocity and position of each particle is updated iteratively based on personal and social experiences. Each particle possesses a local memory in which the best so far achieved experience is stored. Also a global memory keeps the best solution found so far. The sizes of both memories are restricted to one. The local memory represents the personal experience of the particle and the global memory represents the social experience of the swarm. The balance between the effect of the personal and social experiences are maintained using randomized correction coefficients. The philosophy behind the velocity update procedure is to reduce the distance between the particle and the best personal and social known locations. PSO is very easy to implement and there have been many successful implementations in several real world applications. PSO is a population based heuristic approach. It can get stuck in local optima when dealing with complex multimodal functions. This is why accelerating the convergence speed as well as avoiding the local optima are two primary goals in PSO research. Multiple methods and approaches have been suggested to improve the performance of the original PSO in terms of these goals. In [2], these efforts have been divided into four categories. The first category includes the parameter selection methods....

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