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

Akopov, Andranik S.; Beklaryan, A. L.; Thakur, Manoj; Verma, Bhisham Dev

doi:10.17323/1998-0663.2019.1.33.44

Cited by 2 publications

(4 citation statements)

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“…As a result of executing the given procedure, automatic generation of software modules of the synthesized ANN in C++ and Python is provided, which can be used as surrogate models in parallel real-coded genetic algorithms (RCGAs) [2,3,23], and this makes it possible to radically increase the time-efficiency of the optimal solution search procedure for multi-agent socio-economic systems.…”

Section: Is the Number Of Hidden Layers In Mlp;mentioning

confidence: 99%

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Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning

Akopov¹

2023

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This article presents a new approach to modeling and optimizing individual decision-making strategies in multi-agent socio-economic systems (MSES). This approach is based on the synthesis of agent-based modeling methods, machine learning and genetic optimization algorithms. A procedure for the synthesis and training of artificial neural networks (ANNs) that simulate the functionality of MSES and provide an approximation of the values of its objective characteristics has been developed. The feature of the two-step procedure is the combined use of particle swarm optimization methods (to determine the optimal values of hyperparameters) and the Adam machine learning algorithm (to compute weight coefficients of the ANN). The use of such ANN-based surrogate models in parallel multi-agent real-coded genetic algorithms (MA-RCGA) makes it possible to raise substantially the time-efficiency of the evolutionary search for optimal solutions. We have conducted numerical experiments that confirm a significant improvement in the performance of MA-RCGA, which periodically uses the ANN-based surrogate-model to approximate the values of the objective and fitness functions. A software framework has been designed that consists of the original (reference) agent-based model of trade interactions, the ANN-based surrogate model and the MA-RCGA genetic algorithm. At the same time, the software libraries FLAME GPU, OpenNN (Open Neural Networks Library), etc., agent-based modeling and machine learning methods are used. The system we developed can be used by responsible managers.

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Section: Is the Number Of Hidden Layers In Mlp;mentioning

confidence: 99%

“…As examples of DSS belonging to the first type, we can highlight: a software package designed to manage the investment activities of a large oil company [1], a decision support system for environmental and economic planning [2], intelligent transport systems [3][4][5], etc.…”

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confidence: 99%

Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning

Akopov¹

2023

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“…At the same time, if the number of simultaneously evaluated raw materials assets is large (for example, several thousand), then genetic optimization algorithms [23][24][25], aggregated with the simulation model of a production company, should be used to identify and "turn off" such fields.…”

Section: Automation Of Management and Production Processesmentioning

confidence: 99%

“…For example, simulation models developed in AnyLogic and written in the programming Java can be integrated with the applications designed with the use of C++ and MPI (message passing interface) for parallelizing the respective computational procedures. As a result, the simulation model can be aggregated with genetic optimizing algorithms through objective functions, providing the ability to optimize characteristics of the simulated object in real time [23][24][25]. To ensure the software and management pool of available digital twins, it is possible to use an approach based on a service-oriented architecture (SOA).…”

Section: Introductionmentioning

confidence: 99%

Developing digital twins for production enterprises

Makarov¹,

Bakhtizin²,

Beklaryan³

2019

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This article presents a new approach to developing digital twins of production companies with the use of simulation methods. It describes the concept of digital twins as an integrated system that aggregates simulation models, databases and intelligent software modules of the class of genetic optimization algorithms, subsystems of data mining, etc. The article presents examples of simulation models of different production companies, in particular, a typical assembly plant and a typical oil production enterprise. The first company carries out activities to assembly products from individual components with its own individual characteristics. To describe the behavior of such an enterprise, methods of agent and discrete-event modeling are used. The second enterprise produces raw carbohydrate materials at existing fields with individual characteristics. The integrated simulation models thus developed are integrated with a subject-oriented database and optimization modules that facilitate providing a control of the technological and resource characteristics of the respective production enterprises. The development of these models was performed using AnyLogic and Powersim simulation systems that support agent-based modeling and system dynamics methods. We demonstrate here the possibility of creating 'digital twins' for production companies using modern simulation tools.

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

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

Cited by 2 publications

References 14 publications

Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning

Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning

Developing digital twins for production enterprises

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