Metaheuristic optimization for automatic clustering of customer-oriented supply chain data
Cesar Lincoln Cavalcante Mattos, Guilherme A. Barreto, Dennis Horstkemper, Bernd Hellingrath · 2017
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.