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Original scientific paper

https://doi.org/10.17535/crorr.2020.0019

Discrete self-organizing migration algorithm and p-location problems

Jaroslav Janaček ; University of Žilina, Faculty of Management Science and Informatics, Žilina, Slovakia
Marek Kvet ; University of Žilina, Faculty of Management Science and Informatics, Žilina, Slovakia


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Abstract

Mathematical modelling, and integer programming generally, has many practical applications in different areas of human life. Effective and fast solving approaches for various optimization problems play an important role in the decision-making process and therefore, big attention is paid to the development of many exact and approximate algorithms. This paper deals only with a special class of location problems in which given number of facilities are to be chosen to minimize the objective function value. Since the exact methods are not suitable for their unpredictable computational time or memory demands, we focus here on possible usage of a special type of a particle swarm optimization algorithm transformed by discretization and meme usage into so-called discrete self-organizing migrating algorithm. In the paper, there is confirmed that it is possible to suggest a sophisticated heuristic for zero-one programming problem, which can produce near-to-optimal solution in much smaller time than the time demanded by exact methods. We introduce a special adaptation of the discrete self-organizing migration algorithm to the $p$-location problem making use of the path-relinking method. In the theoretical part of this paper, we introduce several strategies of the migration process. To verify their features and effectiveness, a computational study with real-sized benchmarks was performed. The main goal of the experiments was to find the most efficient version of the suggested solving tool.

Keywords

discrete self-organizing migration algorithm; large location problems; meme usage; path-relinking method

Hrčak ID:

248151

URI

https://hrcak.srce.hr/248151

Publication date:

18.12.2020.

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