Metaheuristic optimization for automatic clustering of customer-oriented supply chain data

C. Mattos, G. Barreto, D. Horstkemper, B. Hellingrath
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引用次数: 7

Abstract

In this paper we evaluate metaheuristic optimization methods on a partitional clustering task of a real-world supply chain dataset, aiming at customer segmentation. For this purpose, we rely on the automatic clustering framework proposed by Das et al. [1], named henceforth DAK framework, by testing its performance for seven different metaheuristic optimization algorithm, namely: simulated annealing (SA), genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), cuckoo search (CS) and fireworks algorithm (FA). An in-depth analysis of the obtained results is carried out in order to compare the performances of the metaheuristic optimization algorithms under the DAK framework with that of standard (i.e. non-automatic) clustering methodology.
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面向客户的供应链数据自动聚类的元启发式优化
在本文中,我们评估了针对现实世界供应链数据集的分区聚类任务的元启发式优化方法,旨在细分客户。为此,我们依靠Das等人[1]提出的自动聚类框架(以下命名为DAK框架),通过测试其在模拟退火(SA)、遗传算法(GA)、粒子群优化(PSO)、差分进化(DE)、人工蜂群(ABC)、布谷鸟搜索(CS)和烟花算法(FA)等七种不同的元启发式优化算法上的性能。为了比较DAK框架下的元启发式优化算法与标准(即非自动)聚类方法的性能,对获得的结果进行了深入分析。
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