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Conference paper 17 Citations

A Comparison between NSGA-II and NSGA-III to Solve Multi-Objective Sectorization Problems based on Statistical Parameter Tuning

Teymourifar, Aydin , Rodrigues, Ana Maria , Ferreira, Jose Soeiro
ISLA — Instituto Politécnico de Gestão e Tecnologia
2020 Published by: IEEE
DOI: 10.1109/cscc49995.2020.00020

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Abstract
This paper compares the non-dominated sorting genetic algorithm (NSGA-II) and NSGA-III to solve multiobjective sectorization problems (MO-SPs). We focus on the effects of the parameters of the algorithms on their performance and we use statistical experimental design to find more effective parameters. For this purpose, the analysis of variance (ANOVA), Taguchi design and response surface method (RSM) are used. The criterion of the comparison is the number of obtained nondominated solutions by the algorithms. The aim of the problem is to divide a region that contains distribution centres (DCs) and customers into smaller and balanced regions in terms of demands and distances, for which we generate benchmarks. The results show that the performance of algorithms improves with appropriate parameter definition. With the parameters defined based on the experiments, NSGA-III outperforms NSGA-II.

Keywords

#NSGA-II #NSGA-III #Multi-objective optimization #parameter tuning #experimental design #Taguchi method #response surface methodology #analysis of variance #distribution center location #demand balancing #benchmark generation #Sorting #Taguchi methods #Mathematical optimization #Variance (accounting) #genetic algorithm #Computer science #Design of experiments #Focus (optics) #Algorithm

Bibliographic Details

Publication Type Conference paper
Publication Year 2020
ISBN 9781728165035
Publisher IEEE

ISLA Authors & Affiliations

A
Ana Maria Moreira Rodrigues
anamaria@iscap.ipp.pt ID: 9318-3841-0F19
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