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

Cybernetics and Information Technologies. 2023. Vol. 23. No. 2. P. 87–104.

Andranik S. Akopov, Armen L. Beklaryan, Zhukova A.

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.

Keywords: генетические алгоритмы, методы машинного обучения, Real-coded genetic algorithms, FLAME GPU, FLAME GPU, Particle swarm optimization, Machine learning methods, stochastic simulation model, agent-based modeling of barter and monetary interactions, optimal control in agent models, стохастическая имитационная модель, агентное моделирование бартерных и денежных взаимодействий, оптимальное управление в агентных моделях, роевые алгоритмы