Enhanced PSO Based on Population Initialization and Exploration for the Permutation Flow Shop Scheduling Problem

  • Azhar Y. Abdulhussein Department of Mathematics, College of Science, University of Basrah, Iraq
  • Mohanad AL-Behadili Department of Mathematics, College of Science, University of Basrah, Iraq.
Keywords: Permutation Flow Shop Scheduling Problem, Particle Swarm Optimization, N-NEH Heuristic, Iterated Local Search

Abstract

In this paper, a hybrid PSO (NLPSO*) is adapted to improve the obtained local optimal solution for the Permutation Flow Shop Scheduling Problem (PFSP) with minimizing the makespan. In this method, an improved NEH heuristic called -NEH+ is used to generate a good initial population. Then the PSO is triggered, followed by Iterated Local Search (ILS) to increase the coverage of exploration and exploitation search in the solution space. Both of the -NEH+ and ILS  are simple and efficient algorithms.  A computational study is performed to show the efficiency of the proposed technique. Several of well-known PFSP instances of small, medium, and large sizes were used in this study. The experimental study shows that the NLPSO* algorithm is significantly efficient in reaching better local optimal solutions.

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Published
2022-01-02
How to Cite
Abdulhussein, A., & AL-Behadili, M. (2022). Enhanced PSO Based on Population Initialization and Exploration for the Permutation Flow Shop Scheduling Problem. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(4), Math Page 16-. https://doi.org/10.29304/jqcm.2021.13.4.867
Section
Math Articles