A pair-wise bare bones particle swarm optimization algorithm
Jia Yuan Guo, Yuji Sato · 2017
Bare bones particle swarm optimization (BBPSO) algorithm, a swarm intelligence algorithm, is famous for its easy applying and parameter-free. That is why its principles and applications have been studied by a lot of scholars in recent years. However, quickly losing the diversity of the swarm still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm optimization (PBBPSO) 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. Also, to verify the performance of the proposed algorithm, a set of well-known nonlinear benchmark functions are used in the experiment. Furthermore, severe variants of BBPSO and some other evolutionarily algorithms are also evaluated on the same functions as the control group. Finally, the experiment result and statistical analysis confirm the performance of PBBPSO with nonlinear functions.