A Hybrid Discrete Particle Swarm Optimization for Job Shop Scheduling

The job shop scheduling problem (JSSP) is a well known NP-hard problem, and many algorithms have been presented to solve it, but the results are still unsatisfactory. In this paper, a hybrid discrete particle swarm optimization algorithm based on a two layer population structure is proposed to solve the JSSP, meanwhile add an improved simulated annealing algorithm to increase the ability of finding the global optimum solutions. The experimental results illustrate the high effectiveness of the proposed method, which can avoid prematurity efficiently and be more robust than the PSO and DPSO.