A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight

The inertia weight is often used to control the global exploration and local exploitation abilities of particle swarm optimizers (PSO). In this paper, a group of strategies with multi-stage linearly-decreasing inertia weight (MLDW) is proposed in order to get better balance between the global and local search. Six most commonly used benchmarks are used to evaluate the MLDW strategies on the performance of PSOs. The results suggest that the PSO with W₅ strategy is a good choice for solving unimodal problems due to its fast convergence speed, and the CLPSO with W₅ strategy is more suitable for solving multimodal problems. Also, W₅-CLPSO can be used as a robust algorithm because it is not sensitive to the complexity of problems for solving.