International Journal of Computational Intelligence Systems

Volume 13, Issue 1, 2020, Pages 920 - 940

A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

Authors
M.A. Farag1, *, ORCID, M.A. El-Shorbagy1, 2, ORCID, A.A. Mousa1, 3, ORCID, I.M. El-Desoky1
1 Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt
2 Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
3 Mathematics and Statistics Department, Faculty of Science, Taif University, Taif, Saudi Arabia
*Corresponding author. Email: mai.farag2015@gmail.com
Corresponding Author
M.A. Farag
Received 16 January 2020, Accepted 10 June 2020, Available Online 29 June 2020.
DOI
https://doi.org/10.2991/ijcis.d.200618.001How to use a DOI?
Keywords
Sine-cosine algorithm, Nondominated sorting genetic algorithm, Multiobjective optimization problems, The economic emission dispatch problem, TOPSIS
Abstract

The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. The performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. The operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDP. Finally, TOPSIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision-maker's preference.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
920 - 940
Publication Date
2020/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.200618.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - M.A. Farag
AU  - A.A. Mousa
AU  - M.A. El-Shorbagy
AU  - I.M. El-Desoky
AU  - A.A. Mousa
AU  - I.M. El-Desoky
PY  - 2020
DA  - 2020/06
TI  - A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 920
EP  - 940
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200618.001
DO  - https://doi.org/10.2991/ijcis.d.200618.001
ID  - Farag2020
ER  -