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Article information
2018 , Volume 23, ¹ 5, p.21-36
Garagulova A.K., Gorbacheva D.O., Chirkov D.V.
Comparative analysis of MOGA and NSGA-II on the case study of optimization for the profile of the hydraulic turbine runner
Purpose. The main goal of this article is to compare two popular multi-objective genetic algorithms MOGA and NSGA-II by solving test problems and a practical problem of hydraulic turbine runner optimization. Modification of NSGA-II called NSGA-IIm is also considered. The major problem is to compare convergence rates of approximate solution to the exact Pareto front. Methodology. The genetic algorithms MOGA and NSGA-II are described in detail. The modified algorithm NSGA-IIm is NSGA-II with the recombination and mutation operators taken from the MOGA algorithm. The known problems ZDT3 (2 objectives, 30 and 12 parameters, no constraints) and OSY (2 objectives, 6 parameters, 6 constraints) are taken as test problems. These algorithms are compared by solving runner optimization problem with 24 free parameters and 2 or 3 objectives. To compare the algorithms, a metric characterizing the distance from the approximate Pareto front to the exact one is introduced. Since the algorithms are stochastic in nature, the value of the metric was averaged over 100 runs of the algorithm. In the two-objective runner optimization problem the metric value was averaged over 3 runs of the algorithm. Findings. Solving the test problems, it was found that in the first 50 generations the MOGA algorithm converges faster than other algorithms, but after 50 generations the NSGA-II algorithm has shown the best result. The MOGA algorithm gives an approximate front containing more solutions than NSGA-II. When solving the two-objective and the three-objective runner optimization problem the similar results were obtained. The approximate Pareto front, obtained by the MOGA algorithm, is distributed more uniformly than other algorithms and contains a larger number of solutions. The advantage of the algorithms NSGA-II and NSGA-IIm is a slightly better definition of extreme values of target functionals. However, the obtained differences are not very significant. Originality/value. Obtained results show that both MOGA and NSGA-II are very similar in terms of convergence rate and can be applied for solving complex engineering problems.
[full text]
Keywords: genetic algorithm, NSGA-II, MOGA
doi: 10.25743/ICT.2018.23.5.003
Author(s):
Garagulova Anastasiya Kerimovna
Office: Institute of Computational Technologies of the Siberian Branch of the Russian Academy of Sciences
Address: 630090, Russia, Novosibirsk, Academician M.A. Lavrentiev avenue, 6
E-mail: akgaragulova@gmail.com
Gorbacheva Daria Olegovna
Office: Institute of Computational Technologies of the Siberian Branch of the Russian Academy of Sciences
Address: 630090, Russia, Novosibirsk, Academician M.A. Lavrentiev avenue, 6
E-mail: pankod17@gmail.com
Chirkov Denis Vladimirovich
PhD.
Position: Senior Research Scientist
Office: Institute of Computational Technologies SB RAS
Address: 630090, Russia, Novosibirsk, Ac. Lavrentjev Ave. 6,
Phone Office: (383) 330 73 73
E-mail: chirkov@ict.nsc.ru
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Bibliography link:
Garagulova A.K., Gorbacheva D.O., Chirkov D.V. Comparative analysis of MOGA and NSGA-II on the case study of optimization for the profile of the hydraulic turbine runner // Computational technologies. 2018. V. 23. ¹ 5. P. 21-36
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