Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

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. 103-122.10.1016/j.knosys.2019.03.003Search in Google Scholar

2. Conn, A. R., K. Scheinberg, L. N. Vicente. Introduction to Derivative-Free Optimization. MPS-SIAM Book Series on Optimization. Philadelphia, SIAM, 2009.10.1137/1.9780898718768Search in Google Scholar

3. Audet, C., M. Kokkolaras. Blackbox and Derivative-Free Optimization: Theory, Algorithms and Applications. – Optimization and Engineering, Vol. 17, 2016, No 1, pp. 1-2.10.1007/s11081-016-9307-4Search in Google Scholar

4. Forrester, J. W. Industrial Dynamics – A Major Breakthrough for Decision Makers. – Harvard Business Review, Vol. 36, 1958, No 4, pp. 37-66.Search in Google Scholar

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. 220-230.10.1504/IJCAT.2012.051122Search in Google Scholar

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. 495-509.10.1111/j.1475-3995.2004.00473.xSearch in Google Scholar

7. Keloharju, R., E. F. Wolstenholme. The Basic Concepts of System Dynamics Optimization. – Systems Practice, Vol. 1, 1988, No 1, pp. 65-86.10.1007/BF01059889Search in Google Scholar

8. Dangerfield, B. System Dynamics Models, Optimization. – In: R. Meyers, Ed. Encyclopedia of Complexity and Systems Science. New York, NY, Springer, 2013.10.1007/978-3-642-27737-5_542-4Search in Google Scholar

9. Fletcher, R. Practical Methods of Optimization. 2nd ed. New York, John Wiley & Sons, 1987.Search in Google Scholar

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. 1190-1208.10.1137/0916069Search in Google Scholar

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. 25-35.10.17323/1998-0663.2017.1.25.35Search in Google Scholar

12. Akopov, A. S. Parallel Genetic Algorithm with Fading Selection. – International Journal of Computer Applications in Technology, Vol. 49, 2014, No 3/4, pp. 325-331.10.1504/IJCAT.2014.062368Search in Google Scholar

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.Search in Google Scholar

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. 7-25.10.1016/j.envsoft.2019.02.003Search in Google Scholar

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. 99-118.10.1016/j.ecolmodel.2016.11.012Search in Google Scholar

16. Deep, K., M. Thakur. A New Crossover Operator for Real Coded Genetic Algorithms. – Applied Mathematics and Computation, Vol. 188, 2007, No 1, pp. 895-911.10.1016/j.amc.2006.10.047Search in Google Scholar

17. Herrera, F., M. Lozano. Gradual Distributed Real-Coded Genetic Algorithms. – IEEE Transactions on Evolutionary Computation, Vol. 4, 2000, No 1, pp. 43-63.10.1109/4235.843494Search in Google Scholar

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., 1997.10.1142/2896Search in Google Scholar

19. Kramer, O. A Brief Introduction to Continuous Evolutionary Optimization. – In: Springer Briefs in Computational Intelligence, Springer, 2014.10.1007/978-3-319-03422-5Search in Google Scholar

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. 275-271.10.1109/4235.797969Search in Google Scholar

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. 190-200.10.2478/cait-2019-0011Search in Google Scholar

22. Georgieva, P. Genetic Fuzzy System for Financial Management. – Cybernetics and Information Technologies, Vol. 18, 2018, No 2, pp. 22-35.10.2478/cait-2018-0025Search in Google Scholar

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.Search in Google Scholar

24. Deep, K., M. Thakur. A New Crossover Operator for Real Coded Genetic Algorithms. – Applied Mathematics and Computation, Vol. 188, 2007, No 1, pp. 895-911.10.1016/j.amc.2006.10.047Search in Google Scholar

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.Search in Google Scholar

26. Deep, K., M. Thakur. A New Mutation Operator for Real Coded Genetic Algorithms. – Applied Mathematics and Computation, Vol. 193, 2007, No 1, pp. 211-230.10.1016/j.amc.2007.03.046Search in Google Scholar

27. Metropolis, N., S. Ulam. The Monte Carlo Method. – Journal of the American Statistical Association, Vol. 44, 1949, No 247, pp. 335-341.10.1080/01621459.1949.1048331018139350Search in Google Scholar

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. 49-59.10.1515/cait-2017-0054Search in Google Scholar

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. 3-28.10.1515/cait-2017-0027Search in Google Scholar

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. 784-801.10.1007/s13198-017-0651-3Search in Google Scholar

31. Romasevych, Y., V. A. Loveikin. Novel Multi-Epoch Particle Swarm Optimization Technique. – Cybernetics and Information Technologies, Vol. 18, 2018, No 3, pp. 62-74.10.2478/cait-2018-0039Search in Google Scholar

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. 97-112.10.1016/j.cam.2017.04.047Search in Google Scholar