A novel particle swarm optimisation with mutation breeding - Zhe Liu; Fei Han; Qing-Hua Ling (Connection Science)
The diversity of the population is a key factor for particle swarm optimisation (PSO) when dealing with most optimisation problems. The best previously visited positions of each particle are the exemplar in PSO to guide particle swarm to search, and the diversity of the population can be controlled by these best previously visited positions. Base on this idea of to control the diversity of population to improve the performance of PSO, this paper proposes a novel PSO with mutation breeding (MBPSO), which performs a mutation breeding operation periodically, to control the diversity of the population to improve the global optimisation ability. The mutation breeding operation can be divided into two steps: breeding and mutation. The breeding step is to replace all of best previously visited positions of each particle with the global best previously visited position, and the mutation step is to perform a mutation operation for those new generated best previously visited positions. In addition, we adopt a new updating mechanism of the global best previously position to avoid falling into local optimum. The experimental results on a suit of benchmark functions verifies that the proposed PSO is a competitive algorithm when compare with other PSO variants.