Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Design and Engineering Pub Date : 2023-07-21 DOI:10.1093/jcde/qwad078
Bo Liu, Yongquan Zhou, Qifang Luo, Huajuan Huang
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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.
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基于精英突变策略的非洲秃鹫优化算法求解生产调度问题
生产调度问题是一项具有挑战性的任务,它涉及到将制造资源分配给作业,同时确保满足所有约束条件。PS的关键难点在于确定适当的操作顺序。在这项研究中,我们提出了一种新的优化算法,称为量子启发的精英突变策略非洲秃鹫优化算法(QEMAVOA)来解决这个问题。QEMAVOA是非洲秃鹫优化算法(AVOA)的增强版本,包含三个新的改进策略。首先,为了增强QEMAVOA的多样化能力,在QEMAVOA初始化阶段引入量子双链编码(QDCE)来丰富种群多样性;其次,量子旋转门(QRG)的实施将平衡QEMAVOA的多样化和开发能力,引导秃鹫找到更好的解决方案。最后,为了提高QEMAVOA的可利用性,引入了精英突变(EM)策略。为了评估QEMAVOA的性能,我们将其应用于两个基准调度问题:柔性作业车间调度(FJSP)和并行机器调度(PMS)。结果与文献中现有算法的结果进行了比较。测试结果表明,QEMAVOA在精度、稳定性和收敛速度上都优于比较算法。
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
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.
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