An improved adaptive particle swarm optimization algorithm for job-shop scheduling problem

摘要

This paper presents an improved adaptive particle swarm optimization algorithm (IAPSO) which is inspired from hormone modulation mechanism for solving the minimum makespan problem of job shop scheduling problem (JSP). The environment around swarms and incretion factors are used to modify the updating equations of particle swarm, and the performance of particle swarm optimization is improved. The computational results validate the effectiveness of the proposed IAPSO, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing PSO algorithms reported recently in the literature. By employing IAPSO, machines can be used more efficiently, which means tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.