International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 54 - 66

An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling

Authors
Yingli Li, Jiahai Wang*, ORCID, Zhengwei LiuORCID
School of Mechanical Engineering, Tongji University, Shanghai, 201804, China
*Corresponding author. Email: jhwang@tongji.edu.cn
Corresponding Author
Jiahai Wang
Received 18 July 2020, Accepted 29 October 2020, Available Online 9 November 2020.
DOI
10.2991/ijcis.d.201104.001How to use a DOI?
Keywords
Flexible job-shop scheduling; Multi-objective optimization; Evolutionary algorithm; Local search
Abstract

An adaptive evolutionary algorithm with two-stage local search is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSP). Adaptivity and efficient solving ability are the two main features. An autonomous selection mechanism of crossover operator is designed, which divides individuals into different levels and selects the appropriate one according to the both sides' levels to improve the self-adaptation. In parameter setting, the autonomous determination and adjustment mechanism is proposed, and parameters are adjusted autonomously according to the job scale and iteration number, so as to reduce the complexity of parameter setting and further improve the adaptivity. For improving solving ability, two-stage local search mechanism is designed. The first stage is performed before the evolution operation, so that each individual has more good genes to participate in the following operation. The second stage is performed after the evolution operation to further search the optimal solutions. Finally, a large number of comparative numerical tests are carried out, compared with other excellent algorithms, the proposed algorithm has fewer parameters to be set and stronger solving ability.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
54 - 66
Publication Date
2020/11/09
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.201104.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Yingli Li
AU  - Jiahai Wang
AU  - Zhengwei Liu
PY  - 2020
DA  - 2020/11/09
TI  - An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling
JO  - International Journal of Computational Intelligence Systems
SP  - 54
EP  - 66
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201104.001
DO  - 10.2991/ijcis.d.201104.001
ID  - Li2020
ER  -