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ISSN 2587-814X (print),
ISSN 2587-8158 (online)

Russian version: ISSN 1998-0663 (print),
ISSN 2587-8166 (online)

Maxim Hivintsev 1, Andranik Akopov 1,2
  • 1 National Research University Higher School of Economics, 20 Myasnitskaya Str., Moscow, 101000, Russian Federation
  • 2 Central Economics and Mathematics Institute, Russian Academy of Sciences, 47, Nachimovky Prospect, Moscow, 117418, Russia

Application of multi-agent genetic algorithm for search of optimum strategic and operational decisions

2014. No. 1 (27). P. 23–33 [issue contents]

Maxim Khivintcev - Post-Graduate Student, Department of Business Analytics, Faculty of Business Informatics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: mkhivintsev@hse.ru

Andranik Akopov - Professor, Department of Business Analytics, Faculty of Business Informatics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: aakopov@hse.ru

     The article presents a new approach to applying a multi-agent genetic algorithm (MAGAMO) for search of optimum strategic and operational solutions in large-scale simulation models.
     The purpose of the paper is to develop a simulation model of a referential Internet shop on the basis of the system dynamics methods and to apply the multi-agent genetic algorithm (MAGAMO) for solution a multi-criteria optimizing problem of the strategic and operational control parameters related to the class of large-scale problems. An imitation modeling system Powersim Studio is used for implementing the mathematical model of referential internet shop
     The research is focused on the large-scale multi-criteria optimizing problems run in simulation systems.
     For the solution of such problems, a multi-agent genetic algorithm (MAGAMO) is offered. The feature of this algorithm is the distribution of a set of the system operating parameters between agents on the basis of the preliminary cluster analysis. Each agent represents independent genetic algorithm with its own evolution of the decisions, corresponding to the preset control parameters. Information exchange between the agents functioning in parallel processes is carried out through divided memory of system (a multidimensional database). Here, the central process is responsible for selecting solutions of the highest rank of Pareto. Using a specialized software of Pareto Front Viewer visualization, Pareto’s front is provided.
     The developed simulation model is integrated with algorithm of MAGAMO, system of visualization of Pareto front and a multidimensional database.
     The results of numerical experiments, which have been carried out on real data of the internet shop, have demonstrated high efficiency of the developed multi-agent genetic algorithm for search of optimum solutions in systems of imitating modeling of big dimension.

Citation: Hivintsev Maxim Andreevich, Akopov Andranik Sumbatovich (2014) Primenenie mnogoagentnogo geneticheskogo algoritma dlya poiska optimal'nykh strategicheskikh i operativnykh resheniy [Application of multi-agent genetic algorithm for search of optimum strategic and operational decisions] Biznes-informatika, 1 (27), pp. 23-33 (in Russian)
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