A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions - Jia Guo (International Journal of Networked and Distributed Computing (IJNDC), 2017)
Bare bones particle swarm optimization is a parameter-free swarm intelligence algorithm which is famous for easy applying. It has aroused wide concern of academic circle on its principles and applications in recent years. However, losing the diversity quickly still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm optimization algorithm is proposed in this paper to balance the exploration and exploitation. Moreover, a separate iteration strategy is used in pair-wise operator to enhance the diversity of the swarm. A pair of particles will be placed in different groups and will be applied with different evolutionary strategies. Also, to verify the performance of the proposed algorithm, a set of well-known nonlinear benchmark functions are used in the experiment. Furthermore, several evolutionary algorithms are also evaluated on the same functions as the control group. Finally, the experiment results and statistical analysis confirm the performance of PBBPSO with nonlinear functions.