Developing Software of Global Optimization Method Based on Grey Wolf Optimizer

152

Abstract

This article discusses the development of software that allows to simulate the algorithm of the “Grey Wolf Optimizer” method. This algorithm belongs to the class of metaheuristic algorithms that allow finding a global extremum on a set of admissible solutions. This algorithm is being the most efficiently used in a situation where the cost function is specified in the form of a black box. The algorithm belongs to both bioinspired algorithms and to the class of algorithms of Particle Swarm Optimization. To analyze the efficiency of the algorithm, software was created that allows to vary the parameters of the method. The article contains examples of the program’s work on various test functions. The purpose of the program is to collect and analyze statistical results, making possible to evaluate the final result. The program provides to build graphs that make it possible to make a more thorough assessment of the results obtained. The program has a step-by-step function that allows one to analyze the specifics and features of the algorithm. Analysis of statistical data provides more detailed selection of the parameters of the algorithm.

General Information

Keywords: global optimization algorithm, metaheuristic algorithm, software, GWO, Grey Wolf Optimizer

Journal rubric: Software

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2021110204

For citation: Panteleev A.V., Belyakov I.A. Developing Software of Global Optimization Method Based on Grey Wolf Optimizer. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2021. Vol. 11, no. 2, pp. 59–73. DOI: 10.17759/mda.2021110204. (In Russ., аbstr. in Engl.)

References

  1. Panteleev A.V., Skavinskaya D.V. Metaevristichiskie algoritmy global’noj optimizacii. M.: Vu- zovskaya kniga, 2019.
  2. Karpenko A.P. Sovremennye algoritmy poiskovoj optimizacii. Algoritmy, vdohnovlennye priro- doj. M.: MGTU im. N.E. Baumana, 2014.
  3. Gladkov V.A., Kurejchik V.V. Bioinspirirovannye metody v optimizacii. M.: Fizmatlit, 2009.
  4. Glover F.W., Kochenberger G.A. (eds.). Handbook of Metaheuristics. Boston, MA: Kluwer Academic Publishers, 2003
  5. Floudas C.A., Pardalos P.M. (eds.). Encyclopedia of Optimization. Springer, 2009.
  6. Clerc M. Particle swarm optimization. ISTE Ltd, 2006.
  7. Mirjalili S., Mirjalili S.M., Lewis A. Grey wolf optimizer //Advances in Engineering Software. 2014. Vol. 69. P. 46–61.
  8. Mittal N., Singh U., Sohi B.S. Modified grey wolf optimizer for global engineering optimization // Applied Computational Intelligence and Soft Computing. 2016. Article ID 7950348. http://dx. doi.org/10.1155/2016/7950348
  9. Panteleev A.V. Metaevristicheskie algoritmy optimizacii zakonov upravleniya dinamicheskimi sistemami. M.: Faktorial, 2020.

Information About the Authors

Andrey V. Panteleev, Doctor of Physics and Matematics, Professor, Head of the Department of Mathematical Cybernetics, Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0000-0003-2493-3617, e-mail: avpanteleev@inbox.ru

Ivan A. Belyakov, Undergraduate Student of the Institute of Information Technologiesand Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0000-0003-3457-9914, e-mail: vanbelyakov@yandex.ru

Metrics

Total HTML views: 218
HTML views in previous month: 10
HTML views in current month: 3

Total PDF downloads: 152
PDF downloads in previous month: 6
PDF downloads in current month: 1