Open AccessPerformance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems
Author(s) -
Nurul Izah Anuar,
Muhammad Hafidz Fazli Md Fauadi,
Adi Saptari
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/530/1/012044
Subject(s) - particle swarm optimization , job shop scheduling , mathematical optimization , benchmark (surveying) , discrete space , computer science , continuous optimization , scheduling (production processes) , swarm behaviour , discrete optimization , multi swarm optimization , metaheuristic , optimization problem , mathematics , mathematical analysis , geodesy , geography , schedule , operating system
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.