Radius Particle Swarm Optimization

Abstract

Particle Swarm Optimization (PSO) is a swarm intelligence based and stochastic algorithm to solve the optimization problem. Nevertheless, the traditional PSO has disadvantage from the premature convergence when finding the global optimization. To prevent from falling into the local optimum, we propose the Radius particle swarm optimization (R-PSO) which extends the Particle Swarm Optimization by regrouping the agent particles within the given radius of the circle. It initializes the group of particles, calculates the fitness function, and finds the best particle in that group. The R-PSO employs the group-swarm to keep the swarm diversity and evolution by sharing information from the agent particles which successfully maintain the balance between the global exploration and the local exploitation. Therefore the agent particle guides the neighbour particles to jump out of the local optimum and achieve the global best. The proposed method is tested against the well-known benchmark dataset. The results show that the R-PSO performs better than the traditional PSO in solving the multimodal complex problems.