Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm

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.2019.03.003 Search in Google Scholar

2. 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.2019.02.003 Search in Google Scholar

3. 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.2016.11.012 Search in Google Scholar

4. Akopov, A. S., L. A. Beklaryan. An Agent Model of Crowd Behavior in Emergencies. – Automation and Remote Control, Vol. 76, 2015, No 10, pp. 1817-1827.10.1134/S0005117915100094 Search in Google Scholar

5. 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.2014.062368 Search in Google Scholar

6. 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. 1391-1395.10.1109/SMC.2013.240 Search in Google Scholar

7. Antonini, G., M. Bierlaire, M. Weber. Discrete Choice Models of Pedestrian Walking Behavior. – Transportation Research Part B: Methodological, Vol. 40, 2006, No 8, pp. 667-687.10.1016/j.trb.2005.09.006 Search in Google Scholar

8. 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

9. Beklaryan, G. L., A. S. Akopov, N. K. Khachatryan. Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control. – Cybernetics and Information Technologies, Vol. 19, 2019, No 2, pp. 87-103.10.2478/cait-2019-0017 Search in Google Scholar

10. 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-2019-0011 Search in Google Scholar

11. Bezdek, C. J. Cluster Validity with Fuzzy Sets. – Journal of Cybernetics, Vol. 3, 1974, No 3, pp. 58-73.10.1080/01969727308546047 Search in Google Scholar

12. Bezdek, C. J. Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, Massa, Kluwer Academic Publishers, 1981.10.1007/978-1-4757-0450-1 Search in Google Scholar

13. Bleuler, S., M. Brack, L. Thiele, E. Zitzler. Multiobjective Genetic Programming: Reducing Bloat Using SPEA2. – In: Proc. of 2001 Congress on Evolutionary Computation (IEEE Cat. No 01TH8546), Seoul, South Korea, 2001, pp. 536-543. Search in Google Scholar

14. Breer, V. V., D. A. Novikov, A. D. Rogatkin. Mob Control: Models of Threshold Collective Behavior. – Studies in Systems, Decision and Control, Vol. 85, Springer, Cham, 2017, pp. 1-134.10.1007/978-3-319-51865-7_1 Search in Google Scholar

15. De Ceballos, J. P. G., F. Turégano-Fuentes, D. Perez-Diaz, M. Sanz-Sanchez, C. Martin-Llorente, J. E. Guerrero-Sanz. 11 March 2004: The Terrorist Bomb Explosions in Madrid, Spain-Analysis of the Logistics, Injuries Sustained and Clinical Management of Casualties Treated at the Closest Hospital. – Critical Care, Vol. 9, 2004, No 1, pp. 104-111.10.1186/cc2995106510115693992 Search in Google Scholar

16. Deb, K., A. Pratap, S. Agarwal, T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. – IEEE Transactions on Evolutionary Computation, Vol. 6, 2002, No 2, pp. 182-197.10.1109/4235.996017 Search in Google Scholar

17. Deb, K., L. Thiele, M. Laumanns, E. Zitzler. Scalable Multi-Objective Optimization Test Problems. – In: Proc. of Congress on Evolutionary Computation (CEC-2002), IEEE Press, 2002, pp. 825-830. Search in Google Scholar

18. Deb, K., M. Mohan, S. Mishra. Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. – Evolutionary Computation, Vol. 13, 2005, No 4, pp. 501-525.10.1162/10636560577466689516297281 Search in Google Scholar

19. 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.2006.10.047 Search in Google Scholar

20. 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.2007.03.046 Search in Google Scholar

21. Helbing, D., P. Molnar. Social Force Model for Pedestrian Dynamics. – Physical Review E., Vol. 51, 1995, No 5, pp. 4282-4286.10.1103/PhysRevE.51.42829963139 Search in Google Scholar

22. Helbing, D., I. Farkas, T. Vicsek. Simulating Dynamical Features of Escape Panic. – Nature, No 407, 2000, pp. 487-490.10.1038/3503502311028994 Search in Google Scholar

23. Helbing, D., J. I. Farkas, P. Molnàr, T. Vicsek. Simulation of Pedestrian Crowds in Normal and Evacuation Situations. – In: Proc. of PED01, Pedestrian and Evacuation Dynamics, Springer, Heidelberg, 2002, pp. 21-58. Search in Google Scholar

24. 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. 265-319.10.1023/A:1006504901164 Search in Google Scholar

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

26. 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

27. Li, H., Q. Zhang. Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II. – IEEE Transactions on Evolutionary Computation, Vol. 13, 2009, No 2, pp. 284-302.10.1109/TEVC.2008.925798 Search in Google Scholar

28. Moussaida, M., D. Helbing, G. Theraulaza. How Simple Rules Determine Pedestrian Behavior and Crowd Disasters. – PNAS, Vol. 108, 2011, No 17, pp. 6884-6892.10.1073/pnas.1016507108308405821502518 Search in Google Scholar

29. Olteanu, M., N. Paraschiv, P. Koprinkova-Hristova. Genetic Algorithms vs. Knowledge-Based Control of PHB Production. – Cybernetics and Information Technologies, Vol. 19, 2019, No 2, pp. 104-116.10.2478/cait-2019-0018 Search in Google Scholar

30. Thakur, M., A. Kumar. Optimal Coordination of Directional over Current Relays Using a Modified Real Coded Genetic Algorithm: A Comparative Study. – International Journal of Electrical Power & Energy Systems, Vol. 82, 2016, pp. 484-495.10.1016/j.ijepes.2016.03.036 Search in Google Scholar

31. Thakur, M., S. S. Meghwani, H. Jalota. A Modified Real Coded Genetic Algorithm for Constrained Optimization. – Applied Mathematics and Computation, Vol. 235, 2014, pp. 292-317.10.1016/j.amc.2014.02.093 Search in Google Scholar

32. 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. 257-271.10.1109/4235.797969 Search in Google Scholar