Simplified Particle Swarm Optimization with Backtracking Search

Abstract

In this paper, a simplified particle swarm optimization algorithm based on backtracking search (BSAPSO) is proposed to solve the problems of weak global search ability, easy to be trapped into local optimal solution and difficult to obtain optimal solution in complex problems. Firstly, the velocity term of the standard particle swarm optimization is eliminated, and the exponential random term is introduced to replace the optimal solution of individual particles to simplify the particle iteration process. Then, the special historical backtracking of Backtracking Search algorithm (BSA) is used to increase the population diversity of particle swarm optimization without increasing the complexity of the algorithm. At the same time, the cross-mutation mechanism is introduced to enhance the global search ability of the algorithm. Finally, the validity of the improved algorithm is verified from three aspects: the accuracy of the algorithm, the convergence speed and the statistical test.