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首页> 外文会议>International conference on modelling, indentification and control >Improved NSGA-II Algorithm for Multi-objective Scheduling Problem in Hybrid Flow Shop
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Improved NSGA-II Algorithm for Multi-objective Scheduling Problem in Hybrid Flow Shop

机译：混合流动店中多目标调度问题的改进了NSGA-II算法

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摘要

In this paper, multi-objective optimization for hybrid flow shop scheduling problem is investigated. The delivery time penalty and the load imbalance penalty are taken as the evaluation metrics. We describe the optimization framework for this hybrid flow shop problem and design an improved NSGA-II algorithm for solution searching. Specifically, a multi-objective dynamic adaptive differential evolution algorithm (MODADE) is proposed to enhance the searching efficiency of the basic differential evolution operations. MODADE calculates the similarity between different individuals based on their Hamming distance, and dynamically generates the high-similarity individuals for the population. We further improve the MODADE algorithm by integrating the AP clustering mechanism. We compare the proposed algorithm and compare it with the state-of-the-art solutions. The numerical result shows that the proposed MODADE algorithm outperforms others in terms of the algorithm convergence, the number, and distribution of Pareto solutions.
机译：本文研究了对混合流动店调度问题的多目标优化。交货时间惩罚和负载不平衡惩罚是评估指标。我们描述了该混合流楼问题的优化框架，并设计了一种改进的解决方案搜索算法。具体地，提出了一种多目标动态自适应差分演进算法（塑造）以增强基本差分演化操作的搜索效率。摩擦力根据其汉明距离计算不同的各个人之间的相似性，并且动态地产生人口的高相似性。通过集成AP聚类机制，我们进一步提高了越色达算法。我们比较了所提出的算法，并将其与最先进的解决方案进行比较。数值结果表明，所提出的摩擦机构算法在算法融合，数量和分布方面优于帕累托解决方案的数量。

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