Lev Kazakovtsev

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Genetic algorithm with greedy heuristic is an efficient method for solving large-scale location problems on networks. In addition, it can be adapted for solving continuous problems such as k-means. In this article, authors propose modifications to versions of this algorithm on both networks and continuous space improving its performance. The Probability(More)
We introduce a new problem statement of searching for an optimal wireless network infrastructure spatial configuration based on the information provided by the wireless network equipment. The problem is formulated as a discrete location problem with regional demand and a finite set of candidate solutions. The problem in pseudo-Boolean form is solved with(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)
The multi-facility Weber location problem is transformed into a pseudo-Boolean optimization problem and solved with use of a heuristic random search anytime-algorithm. Fermat-Weber problem in its simplest form (unconstrained, single facility, Euclidean metric) is well investigated. A lot of algorithms are developed for more complex cases. However, the(More)
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)
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)