{"title":"Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems","authors":"Bo Liu, Yongquan Zhou, Qifang Luo, Huajuan Huang","doi":"10.1093/jcde/qwad078","DOIUrl":null,"url":null,"abstract":"\n The Production Scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring that all constraints are satisfied. The key difficulty in PS is determining the appropriate order of operations. In this study, we propose a novel optimization algorithm called the Quantum-inspired African Vultures Optimization Algorithm with an Elite Mutation Strategy (QEMAVOA) to address this issue. QEMAVOA is an enhanced version of the African Vulture Optimization Algorithm (AVOA) that incorporates three new improvement strategies. Firstly, to enhance QEMAVOA's diversification ability, the population diversity is enriched by the introduction of Quantum Double-Chain Encoding (QDCE) in the initialization phase of QEMAVOA. Secondly, the implementation of the Quantum Rotating Gate (QRG) will balance QEMAVOA's diversification and exploitation capabilities, leading the vulture to a better solution. Finally, with the purpose of improving the exploitability of QEMAVOA, the Elite Mutation (EM) strategy is introduced. To evaluate the performance of QEMAVOA, we apply it to two benchmark scheduling problems: Flexible Job Shop Scheduling (FJSP) and Parallel Machine Scheduling (PMS). The results are compared to those of existing algorithms in the literature. The test results reveal that QEMAVOA surpasses comparison algorithms in accuracy, stability, and speed of convergence.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"48 1","pages":"1767-1789"},"PeriodicalIF":4.8000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jcde/qwad078","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The Production Scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring that all constraints are satisfied. The key difficulty in PS is determining the appropriate order of operations. In this study, we propose a novel optimization algorithm called the Quantum-inspired African Vultures Optimization Algorithm with an Elite Mutation Strategy (QEMAVOA) to address this issue. QEMAVOA is an enhanced version of the African Vulture Optimization Algorithm (AVOA) that incorporates three new improvement strategies. Firstly, to enhance QEMAVOA's diversification ability, the population diversity is enriched by the introduction of Quantum Double-Chain Encoding (QDCE) in the initialization phase of QEMAVOA. Secondly, the implementation of the Quantum Rotating Gate (QRG) will balance QEMAVOA's diversification and exploitation capabilities, leading the vulture to a better solution. Finally, with the purpose of improving the exploitability of QEMAVOA, the Elite Mutation (EM) strategy is introduced. To evaluate the performance of QEMAVOA, we apply it to two benchmark scheduling problems: Flexible Job Shop Scheduling (FJSP) and Parallel Machine Scheduling (PMS). The results are compared to those of existing algorithms in the literature. The test results reveal that QEMAVOA surpasses comparison algorithms in accuracy, stability, and speed of convergence.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.