A Chaotic Particle Swarm Optimization algorithm for the jobshop scheduling problem

Abstract

An improved Chaotic Particle Swarm Optimization (CPSO) algorithm for a jobshop scheduling problem, with minimization of makespan as the criterion, is proposed in this research. A real-valued encoding scheme based on a matrix representation is developed, which converts the continuous position value of particles in PSO to the processing order of job operation. A compound chaotic search strategy that integrates both Tent and Logistic chaotic search process is employed to the global best particle to enhance the local searching ability of PSO. In addition, a gaussian disturbance technology is embedded in the CPSO algorithm to improve the diversity of the particles in the swarm. The performance of CPSO is compared with the standard PSO algorithm on a benchmark instance of jobshop scheduling problems. The results show that the proposed CPSO algorithm has a superior performance to the PSO algorithm.