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

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