Simple PSO Algorithm with Opposition-based Learning Average Elite Strategy

Abstract

Due to the slow convergent speed of particle and easily get trapped in the local optima, a novel simple PSO algorithm with opposition-based learning average elite strategy is proposed. In this algorithm, a particle updating formula of the simplified swarm optimization (sPSO) algorithm is adopted. Moreover, the opposition-based learning elite strategy and Gaussian disturbance are exerted on the personal best particles and then replace personal best particle of sPSO with the average of elite opposite solutions with Gaussian disturbance of personal best particles. The adjustment of inertia weight is based on setting a threshold and then the inertia weight selects each mode adaptively according to its current state. A set of experimental results on benchmark functions demonstrate that the proposed PSO algorithm is an effective and efficient approach for optimization problems. Furthermore, the T-test analysis shows that the proposed algorithm is able to achieve better results.

Development of an Optimal Path Planning Using Elite Opposition Based Bat Algorithm for a Mobile Robot

“…Tizhoosh [7] introduced opposition-based learning (OBL) as a method to accelerate the performance of various computational intelligence techniques by increasing the diversity in the population so as to explore different regions that exist in the solution domain [8]. In the context of OBL, generation jumping is used to control the probability of triggering the opposite operation, and it can be considered as a pattern of mutation for encouraging diversity to some extent.…”