Hybrid learning particle swarm optimizer with genetic disturbance

Particle swarm optimizer (PSO) is a population-based stochastic optimization technique which has already been successfully applied to the engineering and other scientific fields. This paper presents a modification of PSO (hybrid learning PSO with genetic disturbance, HLPSO-GD for short) intended to combat the problem of premature convergence observed in many PSO variants. In HLPSO-GD, the swarm uses a hybrid learning strategy whereby all other particles’ previous best information is adopted to update a particle׳s position. Additionally, to better make use of the excellent particle׳s information, the global external archive is introduced to store the best performing particle in the whole swarm. Furthermore, the genetic disturbance (simulated binary crossover and polynomial mutation) is used to cross the corresponding particle in the external archive, and generate new individuals which will improve the swarm ability to escape from the local optima. Experiments were conducted on a set of traditional multimodal test functions and CEC 2013 benchmark functions. The results demonstrate the good performance of HLPSO-GD in solving multimodal problems when compared with the other PSO variants.