A Hybrid Particle Swarm Optimization for Job Shop Scheduling

A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better convergence property is obtained for job shop scheduling with the criterion of minimization the maximum completion time (makespan). Simulation results based on well-known benchmarks and comparisons with standard genetic algorithm demonstrate the feasibility and effectiveness of the proposed hybrid algorithm.