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

1 Pospelov, I. G. Models of Economic Dynamics Based on the Equilibrium of Forecasts of Economic Agents. – Computer Center, Russian Academy of Sciences, Moscow, 2003. Search in Google Scholar

2 Zhukova, A. A., I. G. Pospelov. Monetary and Barter Equilibria in a Stochastic Model of Commodity Exchange between Several Agents. – Computer Center, Russian Academy of Sciences, 2009. Search in Google Scholar

3 Pospelov, I. G. A Model of Random Sales. – Mathematical Notes, Vol. 103, 2018, No 3, pp. 445-459. Search in Google Scholar

4 Zhukova, A. A. Model of the Manufacturer's Behavior when Obtaining Loans and Making Investments at Random Moments in Time. – Mathematical Models and Computer Simulations, Vol. 12, 2020, pp. 933-941. Search in Google Scholar

5 Zhukova, A. A., I. G. Pospelov. Model of Optimal Consumption with Possibility of Taking Loans at Random Moments of Time. – HSE Economic Journal, Vol. 22, 2018, No 3, pp. 330-361. Search in Google Scholar

6 Kiyotaki, N., R. Wright. On Money as a Medium of Exchange. – Journal of Political Economy, Vol. 97, 1989, No 4, pp. 927-954. Search in Google Scholar

7 Kiyotaki, N., R. Wright. A Search-Theoretical Approach to Monetary Economics. – The American Economic Review, Vol. 83, 1993, No 1, pp. 63-77. Search in Google Scholar

8 Akopov, A. S., L. A. Beklaryan, A. L. Beklaryan. Multisector Bounded-Neighborhood Model: Agent Segregation and Optimization of Environment's Characteristics. – Mathematical Models and Computer Simulations, Vol. 14, 2022, No 3, pp. 503-515. Search in Google Scholar

9 Akopov, A. S., L. A. Beklaryan, M. Thakur, D. B. Verma. Parallel Multi-Agent Real-Coded Genetic Algorithm for Large-Scale Black-Box Single-Objective Optimization. – Knowledge-Based Systems, Vol. 174, 2019, pp. 103-122. Search in Google Scholar

10 Akopov, A. S., L. A. Beklaryan, M. Thakur. Improvement of Maneuverability within a Multiagent Fuzzy Transportation System with the Use of Parallel Biobjective Real-Coded Genetic Algorithm. – IEEE Transactions on Intelligent Transportation Systems, Vol. 23, 2022, No 8, pp. 12648-12664. Search in Google Scholar

11 Akopov, A. S., L. A. Beklaryan, A. L. Beklaryan. Simulation-Based Optimization for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation. – Cybernetics and Information Technologies, Vol. 21, 2021, No 3, pp. 127-144. Search in Google Scholar

12 Ali, A. F., M. A. Tawhid. A Hybrid Particle Swarm Optimization and Genetic Algorithm with Population Partitioning for Large Scale Optimization Problems. – Ain Shams Engineering Journal, Vol. 8, 2017, No 2, pp. 191-206. Search in Google Scholar

13 Voronkov, A. D., S. A. K. Diane. Continuous Genetic Algorithm for Grasping an Object of a Priori Unknown Shape by a Robotic Manipulator. – Russian Technological Journal, Vol. 11, 2023, No 1, pp. 18-30. Search in Google Scholar

14 Herrera, F., M. Lozano, J. L. Verdegay. Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. – Artificial Intelligence Review, Vol. 12, 1998, No 4, pp. 265-319. Search in Google Scholar

15 Bonyadi, M. R., Z. Michalewicz. Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review. – Evolutionary Computation, Vol. 25, 2017, No 1, pp. 1-54. Search in Google Scholar

16 Madhumala, R. B., H. Tiwari, D. C. Verma. Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter. – Cybernetics and Information Technologies, Vol. 21, No 1, pp. 62-72. Search in Google Scholar

17 Jin, Y. A Comprehensive Survey of Fitness Approximation in Evolutionary Computation. – Soft Computing, Vol. 9, 2005, pp. 3-12. Search in Google Scholar

18 Richmond, P., D. Walker, S. Coakley, D. Romano. High Performance Cellular Level Agent-Based Simulation with FLAME for the GPU. – Briefings in Bioinformatics, Vol. 11, 2010, No 3, pp. 334-347 Search in Google Scholar

19 Makarov, V. L., A. R. Bakhtizin, G. L. Beklaryan, A. S. Akopov, N. V. Strelkovskii. Simulation of Migration and Demographic Processes Using FLAME GPU. – Business Informatics, Vol. 16, 2022, No 1, pp. 7-21. Search in Google Scholar

20 Beklaryan, A. L., L. A. Beklaryan, A. S. Akopov. Implementation of the Deffuant Model Within the FLAME GPU Framework. – Advances in Systems Science and Applications, Vol. 21, 2021, No 4, pp. 87-99. Search in Google Scholar

21 Kumar, A., K. Deb. Real-Coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems. – Complex Systems, Vol. 9, 1995, pp. 431-454. Search in Google Scholar

22 Deep, K., M. Thakur. A New Crossover Operator for Real Coded Genetic Algorithms. – Applied Mathematics and Computation, Vol. 188, 2007, No 1, pp. 895-911. Search in Google Scholar

23 Deep, K., M. Thakur. A New Mutation Operator for Real Coded Genetic Algorithms. – Applied Mathematics and Computation, Vol. 193, 2007, No 1, pp. 211-230. Search in Google Scholar