A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designed to address the job shop scheduling problem and a mechanism of quorum sensing and communication are presented to improve the foraging performance. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the jobshop scheduling problem. The proposed coevolutionary algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined using a set of benchmark instances with various sizes and levels of hardness and compared with other approaches reported in some existing literatures. The computational results validate the effectiveness of the proposed algorithm.