A Hybrid Particle Swarm Algorithm for Job Shop Scheduling Problems and its Convergence Analysis

Abstract

A hybrid particle swarm algorithm with global asymptotic convergence was proposed, which was used to make up for the deficiencies of resolving Job Shop scheduling problem. In the hybrid particle swarm algorithm, the particle swarm with optimum keeping strategy was applied to search in the global solution space, and the taboo search algorithm was utilized as the local algorithm, which can strengthen the capability of the local search. This article had not only proved the global asymptotic convergence of the hybrid algorithm by Markov chain theory of stochastic processes, but also applied the HPSO algorithm to some typical benchmark job shop scheduling problems and found out the optimums of problems FT10, LA02 and LA19 in a short period, which has demonstrated the effectiveness of the hybrid particle swarm algorithm.