Solving Permutation Flow Shop Scheduling Problem with a Cooperative Multi-swarm PSO Algorithm ⋆

In this paper, an Electoral Cooperative Particle Swarm Optimization (ECPSO) based on several subswarms is presented to solving the Permutation Flow Shop Scheduling Problem (PFSSP). In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution. Firstly, a electoral swarm is generated by the voting of primitive sub-swarms and also participate in evolution of swarm, whose particle candidates come from primitive sub-swarms with variable votes from them. Particles in each sub-swarms and electoral swarm share the only global historical best optimum to enhance the cooperative capability. In addition, 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 sub-swarms and renewing the electoral swarm. The proposed method was applied to well-known benchmark, Taillard’s dataset; the results demonstrated good performances of ECPSO in solving the complex PFSSP.