Application of Hybrid Particle Swarm Optimization Algorithm in Workshop Scheduling Problem

DOI

10.2991/ifmeita-17.2018.71How to use a DOI?

Keywords

Particle Swarm Optimization, Heuristic algorithm, Crossover Operator, workshop problem, Area Search

Abstract

Because of the traditional genetic algorithm is difficult to consider both the quality of solution and the efficiency of convergence in solving the problem of constrained optimization in workshop scheduling problem. In this paper, we use the method of working procedure coding to generate feasible scheduling and learn from the single point crossover method of genetic algorithm to generate crossover operator which based on workpiece as the particle update mode. Then the improved Particle Swarm Optimization Algorithm will adopt in solving workshop scheduling problem, and enhance the particle convergence efficiency of particle swarm by using local search in the algorithm. We proved that the improved hybrid particle swarm optimization algorithm has a good performance in solving the workshop scheduling problem through the simulation experiments on the typical scheduling problems.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guitang Wang
AU  - Zhisheng Chen
AU  - Wenjie Liang
AU  - Chaoqiong Yang
PY  - 2018/02
DA  - 2018/02
TI  - Application of Hybrid Particle Swarm Optimization Algorithm in Workshop Scheduling Problem
BT  - Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)
PB  - Atlantis Press
SP  - 420
EP  - 426
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-17.2018.71
DO  - 10.2991/ifmeita-17.2018.71
ID  - Wang2018/02
ER  -