Potential and dynamics-based Particle Swarm Optimization

Abstract— The Particle Swarm Optimization (PSO) algorithm is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper proposes a novel PSO algorithm, based on the potential field and the motion dynamics model. It is assumed that particles form potential fields and each particle has its own mass. The potential filed and mass are modeled by the particles’ fitness value. By using these fitness based models, the proposed algorithm performs well, in particular, in avoiding the local minima compare to the original PSO. The proposed PDPSO successfully solves minimization problems of complex test functions.

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Hyungmin Park, Jong-Hwan Kim
• Keywords: Artificial Intelligence, CEC 2008, Particle Swarm Optimization, Potential fields, Stochastic Evolutionary Computation