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Algorithms with greedy heuristic procedures for mixture probability distribution separation

For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). | Yugoslav Journal of Operations Research xx (), Number nn, zzz–zzz DOI: https://doi.org/10.2298/YJOR171107030K ALGORITHMS WITH GREEDY HEURISTIC PROCEDURES FOR MIXTURE PROBABILITY DISTRIBUTION SEPARATION Lev KAZAKOVTSEV Department of Systems Analysis and Operations Research, Reshetnev University, prosp.Krasnoyarskii Rabochii 31, Krasnoyarsk, 660037, Russian Federation levklevk@gmail.com Dmitry STASHKOV Department of Systems Analysis and Operations Research, Reshetnev University, prosp.Krasnoyarskii Rabochii 31, Krasnoyarsk, 660037, Russian Federation stashkov@ngs.ru Mikhail GUDYMA Department of Systems Analysis and Operations Research, Reshetnev University, prosp.Krasnoyarskii Rabochii 31, Krasnoyarsk, 660037, Russian Federation darfai04@gmail.com Vladimir KAZAKOVTSEV Department of Computer Educational Technologies, ITMO University, Kronverksky Pr. 49, St. Petersburg, 197101, Russian Federation vokz@bk.ru Received: November 2017 / Accepted: November 2018 Abstract: For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighbourhoods to search for better solutions. The results of computational experiments made on classical data sets and the testings of production batches of semiconductor devices shipped for the space industry demonstrate that new algorithms allow us to obtain better 2 L. Kazakovtsev, et al. / Algorithms with Greedy Heuristic Procedures results, higher values of the log likelihood objective function, in comparison with the EM algorithm