References
- 1. Akopov, A. S., L. A. Beklaryan, M. Thakur, B. D. Verma. Parallel Multi-Agent Real-Coded Genetic Algorithm for Large-Scale Black-Box Single-Objective Optimisation. – Knowledge-Based Systems, Vol. 174, 2019, pp. 103-122.
- 2. Conn, A. R., K. Scheinberg, L. N. Vicente. Introduction to Derivative-Free Optimization. MPS-SIAM Book Series on Optimization. Philadelphia, SIAM, 2009.
- 3. Audet, C., M. Kokkolaras. Blackbox and Derivative-Free Optimization: Theory, Algorithms and Applications. – Optimization and Engineering, Vol. 17, 2016, No 1, pp. 1-2.
- 4. Forrester, J. W. Industrial Dynamics – A Major Breakthrough for Decision Makers. – Harvard Business Review, Vol. 36, 1958, No 4, pp. 37-66.
- 5. Akopov, A. S. Designing of Integrated System-Dynamics Models for an Oil Company. – International Journal of Computer Applications in Technology, Vol. 45, 2012, No 4, pp. 220-230.
- 6. Ge, Y., J. B. Yang, N. Proudlove, M. Spring. System Dynamics Modelling for Supply-Chain Management: A Case Study on a Supermarket Chain in the UK. – International Transactions in Operational Research, Vol. 11, 2004, No 5, pp. 495-509.
- 7. Keloharju, R., E. F. Wolstenholme. The Basic Concepts of System Dynamics Optimization. – Systems Practice, Vol. 1, 1988, No 1, pp. 65-86.
- 8. Dangerfield, B. System Dynamics Models, Optimization. – In: R. Meyers, Ed. Encyclopedia of Complexity and Systems Science. New York, NY, Springer, 2013.
- 9. Fletcher, R. Practical Methods of Optimization. 2nd ed. New York, John Wiley & Sons, 1987.
- 10. Byrd, R., P. Lu, J. Nocedal, C. Zhu. A Limited Memory Algorithm for Bound Constrained Optimization. – SIAM Journal on Scientific Computing, Vol. 16, 1995, No 5, pp. 1190-1208.
- 11. Khachatryan, N. K., A. S. Akopov. Model for Organizing Cargo Transportation with an Initial Station of Departure and a Final Station of Cargo Distribution. – Business Informatics, Vol. 1, 2017, No 39, pp. 25-35.
- 12. Akopov, A. S. Parallel Genetic Algorithm with Fading Selection. – International Journal of Computer Applications in Technology, Vol. 49, 2014, No 3/4, pp. 325-331.
- 13. Akopov, A. S., M. A. Hevencev. A Multi-Agent Genetic Algorithm for Multi-Objective Optimization. – In: Proc. of IEEE International Conference on Systems, Man and Cybernetics, Manchester: IEEE, 2013, pp. 1391-1395.
- 14. Akopov, A. S., L. A. Beklaryan, A. K. Saghatelyan. Agent-Based Modelling of Interactions between air Pollutants and Greenery Using a Case Study of Yerevan, Armenia. – Environmental Modelling and Software, Vol. 116, 2019, pp. 7-25.
- 15. Akopov, A. S., L. A. Beklaryan, A. K. Saghatelyan. Agent-Based Modelling for Ecological Economics: A Case Study of the Republic of Armenia. – Ecological Modelling, Vol. 346, 2017, pp. 99-118.
- 16. 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.
- 17. Herrera, F., M. Lozano. Gradual Distributed Real-Coded Genetic Algorithms. – IEEE Transactions on Evolutionary Computation, Vol. 4, 2000, No 1, pp. 43-63.
- 18. E. Sanchez, T. Shibata, L. A. Zadeh, Eds. Genetic Algorithms and Fuzzy Logic Systems. Vol. 7. Soft Computing Perspectives. River Edge, NJ, USA, World Scientific Publishing Co., Inc., 1997.
- 19. Kramer, O. A Brief Introduction to Continuous Evolutionary Optimization. – In: Springer Briefs in Computational Intelligence, Springer, 2014.
- 20. Zitzler, E., L. Thiele. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. – IEEE Transactions on Evolutionary Computation, Vol. 3, 1999, No. 4, pp. 275-271.
- 21. Belev, B., D. Dimitranov, A. Spasov, A. Ivanov. Application of Information Technologies and Algorithms in Ship Passage Planning. – Cybernetics and Information Technologies, Vol. 19, 2019, No 1, pp. 190-200.
- 22. Georgieva, P. Genetic Fuzzy System for Financial Management. – Cybernetics and Information Technologies, Vol. 18, 2018, No 2, pp. 22-35.
- 23. Beklaryan, A. L., A. S. Akopov. Simulation of Agent-Rescuer Behaviour in Emergency Based on Modified Fuzzy Clustering. – In: Proc. of International Joint Conference on Autonomous Agents and Multigene Systems, AAMAS, 2016, pp. 1275-1276.
- 24. 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.
- 25. 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.
- 26. 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.
- 27. Metropolis, N., S. Ulam. The Monte Carlo Method. – Journal of the American Statistical Association, Vol. 44, 1949, No 247, pp. 335-341.
- 28. Karaivanova, A., A. Alexandrov, T. Gurov, S. Ivanovska. On the Monte Carlo Matrix Computations on Intel MIC Architecture. – Cybernetics and Information Technologies, Vol. 17, 2017, No 5, pp. 49-59.
- 29. Kumar, A., D. Kumar, S. K. Jarial. A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering. – Cybernetics and Information Technologies, Vol. 17, 2017, No 3, pp. 3-28.
- 30. Kumar, A., M. Thakur, G. Mittal. A New Ants Interaction Scheme for Continuous Optimization Problems. – International Journal of Systems Assurance Engineering, Vol. 9, 2018, No 4, pp. 784-801.
- 31. Romasevych, Y., V. A. Loveikin. Novel Multi-Epoch Particle Swarm Optimization Technique. – Cybernetics and Information Technologies, Vol. 18, 2018, No 3, pp. 62-74.
- 32. Noack, M. M., S. W. Funke. Hybrid Genetic Deflated Newton Method for Global Optimisation. – Journal of Computational and Applied Mathematics, Vol. 325, 2017, pp. 97-112.