A New Cooperative Particle Swarm Optimizer and its Application in Permutation Flow Shop Scheduling Problem

In this study, a new variant of Particle Swarm Optimization, Electoral Cooperative PSO (ECPSO), is presented and applied into solving the Permutation Flow Shop Scheduling Problem (PFSSP). Firstly, an electoral swarm is generated by the voting of primitive sub-swarms and also participates in evolution of swarm, whose particle candidates come from primitive sub-swarms with variable votes from them. Besides, a fast fitness computation method using processing time matrix of a valid schedule is also imported to accelerate the calculation of makespan function. On the other hand, in order to prevent trapping into local optimization, a disturbance factor mechanism is imported to check the particles movements for resetting the original subswarms and renewing the electoral swarm. To test the basic use and performance of ECPSO, some experiments on function optimization are executed on functions with unfixed and fixed numbers of dimensions. The proposed method was also applied to well-known benchmark of PFSSP, Taillard dataset; the results demonstrated good performances and robustness of ECPSO compared to some versions of PSO.