Application of collaborative filtering methods in the problem of predicting the performance of population optimization algorithms
( Pp. 11-25)

In this paper we propose an approach to solving the problem of choosing the most efficient algorithm for solving a given continuous optimization problem, based on the using of collaborative filtering methods. A prototype of a software system based on a set of the most popular population optimization algorithms and a system of test objective functions for continuous optimization problems is described. The implementation of several methods for predicting the performance of a given algorithm is considered. The results of computational experiments and comparison of the considered methods are presented.

Ershov N.M., Nikitina O.P., (2021), APPLICATION OF COLLABORATIVE FILTERING METHODS IN THE PROBLEM OF PREDICTING THE PERFORMANCE OF POPULATION OPTIMIZATION ALGORITHMS. Computational Nanotechnology, 1 => 11-25.

Poluyan S.V., Ershov N.M. Primenenie parallel nykh evolyutsionnykh algoritmov optimizatsii v zadachakh strukturnoy bioinformatiki // Vestnik UGATU. 2017. T. 21. № 4. S. 143-152.
Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii. M.: Izd-vo MGTU im. N.E. Baumana, 2014.
Melville P., Mooney R., Nagarajan R. Content-boosted collaborative filtering for improved recommendations. University of Texas, USA: AAAI-02, Austin, TX, USA, 2002. Rp. 187-192.
Xiaoyuan Su, Taghi M. Khoshgoftaar, a survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009. Article ID: 421425. Rp. 1-19.
Falk K. Practical recommender systems. Manning Publications, 2019.
Guohua Wu, Mallipeddi R., Suganthan P. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. 2016.
Kirkpatrick S., Gelatt C., Vecchi M. Optimization by simulated annealing // Science. 1983, May 13. No. 220 (4598). Pp. 671-680.
Whitley D. A genetic algorithm tutorial // Statistics and Computing. 1994. No. 4 (2). Pp. 65-85.
Kennedy J., Eberhart R. Particle swarm optimization // Proceedings of IEEE International Conference on Neural Networks. 1995. Pp. 1942-1948.
Passino K. Biomimicry of bacterial foraging for distributed optimization and control // IEEE Control Systems Magazine. 2002. No. 22. Pp. 52-67.
Pham D., Ghanbarzadeh A., Koc E. et al. The bees algorithm - a novel tool for complex optimization problems // Proceedings of IPROMS 2006 Conference. Pp. 454-461.
Storn R., Price K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces // Journal of Global Optimization. 1997. No. 11 (4). Pp. 341-359.
MacQueen J.B. Some methods for classification and analysis of multivariate observations // Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967. Pp. 281-297.
Kohonen T. Self-organized formation of topologically correct feature maps // Biological Cybernetics. 1982. No. 43 (1). Pp. 59-69.
Cover T., Hart P. Nearest neighbor pattern classification // IEEE Transactions on Information Theory. 1967. Vol. 13. No. 1. Pp. 21-27.

recommender systems, optimization, evolutionary algorithms, swarm intelligence methods.