Augmented Gray Wolf-Cuckoo Algorithm-Based Research on Flexible Job-Shop Scheduling

DOI

10.2991/978-94-6463-447-1_37How to use a DOI?

Keywords

Flexible job shop scheduling; Augmented Gray Wolf-Cuckoo algorithm; Cuckoo algorithm; Reverse learning strategy

Abstract

The paper proposes an Augmented Gray Wolf-Cuckoo algorithm (AGWO-CS) to improve the Gray Wolf (GWO) algorithm's performance in flexible job-shop scheduling. AGWO-CS achieves this by incorporating a reverse learning strategy during initial population generation, optimizing exploration and exploitation of the search space. It further refines search parameters using the Cuckoo algorithm, leveraging Gray Wolf's enhancements for increased flexibility. Comparative analysis with Particle Swarm Optimization (PSO) algorithm and (Genetic Algorithm) GA reveals AGWO-CS's superior optimization, convergence, global search, and local search capabilities.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ruonan Peng
AU  - Chengjun Ji
PY  - 2024
DA  - 2024/07/14
TI  - Augmented Gray Wolf-Cuckoo Algorithm-Based Research on Flexible Job-Shop Scheduling
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 338
EP  - 345
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_37
DO  - 10.2991/978-94-6463-447-1_37
ID  - Peng2024
ER  -