用并行混合遗传算法优化基于随机Agent的货物交换模型的特性

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Cybernetics and Information Technologies Pub Date : 2023-06-01 DOI:10.2478/cait-2023-0015
A. Akopov, A. Beklaryan, A. Zhukova
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引用次数: 1

摘要

摘要考虑到优化环境特征和个体决策策略的可能性,提出了一种新的方法来建模多个主体之间货物交换的随机过程。所提出的模型使得在每个主体的个体水平上选择达成易货和货币交易的时刻时能够形成最优状态,从而最大化效用函数。将基于著名启发式算子的进化选择方法与群体优化和机器学习方法相结合,提出了一种新的并行混合实数编码遗传算法和粒子群优化(RCGA-PSO)。与其他方法相比,该算法具有最佳的时间效率和准确性。所开发的算法和模型的软件实现是使用FLAME GPU框架进行的。证明了使用RCGA-PSO算法优化环境特征的可能性,以及参与易货和货币互动的代理人做出个人决策的策略。
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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 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.
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
期刊最新文献
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