A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems
- https://doi.org/10.2991/ijcis.d.200618.001How to use a DOI?
- Sine-cosine algorithm, Nondominated sorting genetic algorithm, Multiobjective optimization problems, The economic emission dispatch problem, TOPSIS
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.
- © 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 -