Multi-Objective Evolutionary Algorithm based on Decomposition with Adaptive Adjustment of Control Parameters to Solve the Bi-Objective Internet Shopping Optimization Problem (MOEA/D-AACPBIShOP)
Título del documento: Multi-Objective Evolutionary Algorithm based on Decomposition with Adaptive Adjustment of Control Parameters to Solve the Bi-Objective Internet Shopping Optimization Problem (MOEA/D-AACPBIShOP)
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000607887
ISSN: 1405-5546
Autores: 1
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Instituciones: 1National Technological Institute of Mexico, Technological Institute of Madero City, México
2National Technological Institute of Mexico, Technological Institute of Leon, México
Año:
Periodo: Abr-Jun
Volumen: 28
Número: 2
Paginación: 727-738
País: México
Idioma: Inglés
Resumen en inglés The main contribution of this paper is the implementation of a multi-objective evolutionary algorithm based on decomposition with adaptive adjustment of control parameters applied to the bi-objective problem of Internet shopping (MOEA/D-AACPBIShOP). For this variant of the IShOP, the minimization of the cost and shipping time of the shopping list is considered. The proposed MOEA/D-AACPBIShOP algorithm produces an approximate Pareto set on a total of nine of instances with real-world data classified as small, medium, and large. The instances are obtained using the Web Scraping technique, extracting some information attributes of technological products from the Amazon site. This optimization problem is a very little studied variant of the Internet Shopping Problem (IShOP). The proposed algorithm is compared with two multi-objective algorithms: A Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the basic MOEA/D version. The results demonstrate that the three algorithms studied have a similar statistical performance with respect to the quality of the solutions they provide. To make a comparison, these algorithms are evaluated using three metrics: Hypervolume, Generalized Dispersion, and Inverted Generational Distance. On the other hand, the Wilcoxon and Friedman non-parametric tests validate the obtained results with a 5% significance level.
Keyword: Multi-objective,
Approximate Pareto front,
Evolutionary algorithm,
Web scraping,
Bi-objective
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