An Estimation of Distribution Particle Swarm Optimization Algorithm

Abstract. In this paper we present an estimation of distribution particle swarm optimization algorithm that borrows ideas from recent developments in ant colony optimization. In the classical particle swarm optimization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has no means to exploit its collective memory (represented by the array of pbests) to guide its search. This causes a re-exploration of already known bad regions of the search space, wasting costly function evaluations. In our approach, we use the swarm's collective memory to estimate the distribution of promising regions in the search space and probabilistically guide the particles' movement towards them. Our experiments show that this approach is able to find similar or better solutions than the standard particle swarm optimizer with fewer function evaluations.

• Type: Conference
• Year: 2006
• Where: ANTSW
• Authors: Mudassar Iqbal, Marco Antonio Montes de Oca
• Keywords: ANTSW 2006, Artificial Intelligence, Particle Swarm, Particle Swarm Optimization, Swarm Optimization Algorithm