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

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

Andranik Akopov1, Armen Beklaryan 1,2, Manoj Thakur3, Bhisham Verma3
  • 1 Central Economics and Mathematics Institute, Russian Academy of Sciences, 47, Nachimovky Prospect, Moscow, 117418, Russia
  • 2 National Research University Higher School of Economics, 20 Myasnitskaya Str., Moscow, 101000, Russian Federation
  • 3 Indian Institute of Technology Mandi , Mandi, Himachal Pradesh, 175005, India

Developing parallel real-coded genetic algorithms for decision-making systems of socio-ecological and economic planning

2019. No. 1 Vol.13. P. 33–44 [issue contents]

      This article presents a new approach to designing decision-making systems for socio-economic and ecological planning using parallel real-coded genetic algorithms (RCGAs), aggregated with simulation models by objective functions. A feature of this approach is the use of special agent-processes, which are autonomous genetic algorithms (GAs) acting synchronously in parallel streams and exchanging periodically by the best potential decisions. This allows us to overcome the premature convergence problem in local extremums. In addition, it was shown that the combined use of different crossover and mutation operators significantly improves the time efficiency of RCGAs, as well as the quality of the decisions obtained (proximity to optimum), providing a more diverse population of potential decisions (individuals).
      In this paper, several suggested crossover and mutation operators are used, in particular, a modified simulated binary crossover (MSBX) and scalable uniform mutation operator (SUM), which is based on quantization of the feasible region of the search space (dividing the feasible region on small subranges with equal lengths) while taking into account the common amount of interacting agent-processes and the maximum number of internal iterations of GAs forming potential decisions through selection, crossover and mutation. Such a functional dependence of the parameters of heuristic operators on the corresponding process characteristics, aggregated with the combined probabilistic use of various crossover and mutation operators, makes it possible to get maximum effect from the multi-processes architecture. As a result, thecomputational possibilities of RCGAs for solving large-scale optimization problems (hundreds and thousands of decision variables, multiple objective functions) become dependent only on the physical characteristics of the existingcomputing clusters. This makes it possible to efficiently use supercomputer technologies.
      An important advantage of the proposed system is the implemented integration between the developed parallel RCGA (implemented in C++ and MPI) and the simulation modelling system AnyLogic (Java) using JNI technology. Such an approach allows one to synthesize real world optimization problems in decision-making systems of socio-economic and ecological planning, using simulation methods supported by AnyLogic. The result is an effective solution to single-objective and multi-objective optimization tasks of large dimension, in which the objective functionals are the result of simulation modeling and cannot be obtained analytically.


Akopov A.S., Beklaryan A.L., Thakur M., Verma B.D. (2019)Developing parallel real-coded genetic algorithms for decision-making systems of socio-ecological and economic planning.Business Informatics, vol. 13, no 1, pp. 33–44    DOI: 10.17323/1998-0663.2019.1.33.44

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