Study and Application of Glowworm Swarm Optimization in Flexible Job Shop Scheduling

DOI

10.2991/icaita-18.2018.26How to use a DOI?

Keywords

Job-Shop Scheduling (JSP) issue; Glowworm Swarm Optimization (GSO); Particle Swarm Optimization (PSO); Permutation-based Differential Evolution (PDE); self-adaptation

Abstract

For satisfying performances of the job-shop scheduling (JSP) issues and as for those characteristics (such as easily falling into local optimum, insufficient stability and slower convergence rate) of the standard GSO, a PDE framework taking advantages of good global search performance, simple implementation and rapid convergence rate and a self-adaptive mechanism based on the S-shape variable-step for its improvement so that the balance between global search and local mining may be coordinated and its stability and convergency may be improved. Simulation experiments were carried out to our hybrid GSO based on the Brandimarte international standard examples and comparison and analysis were performed to the experimental results to verify its reliability and applicability.

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  - Hongtao Wang
AU  - Dailin Xu
AU  - Chun Yan
AU  - Hao Pan
PY  - 2018/03
DA  - 2018/03
TI  - Study and Application of Glowworm Swarm Optimization in Flexible Job Shop Scheduling
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 100
EP  - 107
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.26
DO  - 10.2991/icaita-18.2018.26
ID  - Wang2018/03
ER  -