Performance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems

The Particle Swarm Optimization (PSO) is an optimization method that was modeled based on the social behavior of organisms, such as bird flocks or swarms of bees. It was initially applied for cases defined over continuous spaces, but it can also be modified to solve problems in discrete spaces. Such problems include scheduling problems, where the Job-shop Scheduling Problem (JSP) is among the hardest combinatorial optimization problems. Although the JSP is a discrete problem, the continuous version of PSO has been able to handle the problem through a suitable mapping. Subsequently, its modified model, namely the discrete PSO, has also been proposed to solve it. In this paper, the performance of continuous and discrete PSO in solving JSP are evaluated and compared. The benchmark tests used are FT06 and FT10 problems available in the OR-library, where the goal is to minimize the maximum completion time of all jobs, i.e. the makespan. The experimental results show that the discrete PSO outperforms the continuous PSO for both benchmark problems.