<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the environment's characteristics and individual decision-making strategies. The proposed model makes it possible to form optimal states when choosing the moments of concluding barter and monetary transactions at the individual level of each agent maximizing the utility function. A new parallel hybrid Real-Coded Genetic Algorithm and Particle Swarm Optimization (RCGA-PSO) has been developed, combining methods of evolutionary selection based on well-known heuristic operators with methods of swarm optimization and machine learning. The algorithm is characterized by the best time efficiency and accuracy in comparison with other methods. The software implementation of the developed algorithm and model has been performed using the FLAME GPU framework. The possibility of using the RCGA-PSO Algorithm to optimize the characteristics of the environment and strategies for making individual decisions by agents involved in barter and monetary interactions is demonstrated.</p>
</abstract>

A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the environment's characteristics and individual decision-making strategies. The proposed model makes it possible to form optimal states when choosing the moments of concluding barter and monetary transactions at the individual level of each agent maximizing the utility function. A new parallel hybrid Real-Coded Genetic Algorithm and Particle Swarm Optimization (RCGA-PSO) has been developed, combining methods of evolutionary selection based on well-known heuristic operators with methods of swarm optimization and machine learning. The algorithm is characterized by the best time efficiency and accuracy in comparison with other methods. The software implementation of the developed algorithm and model has been performed using the FLAME GPU framework. The possibility of using the RCGA-PSO Algorithm to optimize the characteristics of the environment and strategies for making individual decisions by agents involved in barter and monetary interactions is demonstrated.

Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm

MIREA – Russian Technological University

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the...

Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm

Online veröffentlicht: 12. Juni 2023

Seitenbereich: 87 - 104

Eingereicht: 15. März 2023

Akzeptiert: 18. Apr. 2023

DOI: https://doi.org/10.2478/cait-2023-0015

Schlüsselwörter
Stochastic simulation model, agent-based modeling of barter and monetary interactions, optimal control in agent models, real-coded genetic algorithms, machine learning methods, particle swarm optimization, FLAME GPU

© 2023 Andranik S. Akopov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm

Online veröffentlicht: 12. Juni 2023

Seitenbereich: 87 - 104

Eingereicht: 15. März 2023

Akzeptiert: 18. Apr. 2023

DOI: https://doi.org/10.2478/cait-2023-0015

SchlüsselwörterStochastic simulation model, agent-based modeling of barter and monetary interactions, optimal control in agent models, real-coded genetic algorithms, machine learning methods, particle swarm optimization, FLAME GPU

© 2023 Andranik S. Akopov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Stochastic simulation model, agent-based modeling of barter and monetary interactions, optimal control in agent models, real-coded genetic algorithms, machine learning methods, particle swarm optimization, FLAME GPU