TY - GEN
T1 - A comparison between NSGA-II and NSGA-III to solve multi-objective sectorization problems based on statistical parameter tuning
AU - Teymourifar, Aydin
AU - Rodrigues, Ana Maria
AU - Ferreira, Jose Soeiro
N1 - Funding Information:
This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds of the Portuguese funding agency, FCT - Fundac¸ão para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-031671.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Analysis of Variance
KW - Design of Experiments
KW - NSGA-II
KW - NSGA-III
KW - Response Surface Method
KW - Sectorization
KW - Statistically Parameter Tuning
KW - Taguchi Method
UR - https://www.scopus.com/pages/publications/85105327729
U2 - 10.1109/cscc49995.2020.00020
DO - 10.1109/cscc49995.2020.00020
M3 - Conference contribution
AN - SCOPUS:85105327729
T3 - Proceedings - 24th International Conference on Circuits, Systems, Communications and Computers, CSCC 2020
SP - 64
EP - 74
BT - Proceedings - 24th international conference on circuits, systems, communications and computers, CSCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Conference on Circuits, Systems, Communications and Computers, CSCC 2020
Y2 - 19 July 2020 through 22 July 2020
ER -