CAPSO: centripetal accelerated particle swarm optimization

Abstract

Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch of meta-heuristic algorithms that imitate the behavior of insects, birds, fishes, and other natural phenomena to find solutions for complex optimization problems. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton's laws of motion, the centripetal accelerated particle swarm optimization (CAPSO) scheme, is introduced. CAPSO accelerates the learning and convergence of optimization problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. These algorithms are evaluated using nonlinear benchmark functions, and the results are compared with the gravitational search algorithm (GSA) and PSO in both the real and the binary search spaces. Moreover, the performance of CAPSO in solving the functions is compared with some well-known PSO algorithms in the literature. The experimental results showed that the proposed methods enhance the performance of PSO in terms of convergence speed, solution accuracy and global optimality.