Lev Kazakovtsev

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Authors investigate the p-median location problem on networks and propose a heuristic algorithm which is based on the probability changing method (a special case of the genetic algorithm) for an approximate solution to the problem. The ideas of the algorithm are proposed under the assumption that, in the large-scale networks with comparatively small edge(More)
Authors propose new genetic algorithm for solving the planar p-median location problem and k-means clustering problem. The ideas of the algorithm are based on the genetic algorithm with greedy heuristic for the p-median problem on networks and information bottleneck (IB) clustering algorithms. The proposed algorithm uses the standard k-means procedure or(More)
In this paper, we investigate application of various options of algorithms with greedy agglomerative heuristic procedure for object clustering problems in continuous space in combination with various local search methods. We propose new modifications of the greedy agglomerative heuristic algorithms with local search in SWAP neighborhood for the p-medoid(More)
The the random search methods are implemented to solve the wide variety of the large­scale discrete optimization problems when the implementation of the exact solution approaches is impossible due to large computational demands. Initially designed for unconstrained optimization, the variant probabilities method (MIVER) [1, 3] allows to find the approximate(More)