Bare bone particle swarm optimization with integration of global and local learning strategies

Bare bone particle swarm optimization (BPSO) possesses self-adapting property and uses fewer parameters resulted in simple implementation and free parameter-tuning. Inevitably, it also tends to converges prematurely, especially for problems with multiple extremes. In this paper, a new method combining global and local learning strategy used in traditional particle swarm optimization (PSO) is devised to improve the performance of the bare bone particle swarm optimization. According to the integration, two variants are proposed. Method is simple and the results are fruitful. Tested on a suite of benchmark functions, unimodal and multimodal functions, justifies the feasibility of the strategy. Both solution quality and convergent speed are better than traditional bare bone particle swarm optimizer.