De Montfort University

Michalis Mavrovouniotis (BSc, MSc, PhD)

Research Associate


photo of Michalis Mavrovouniotis

Centre for Computational Intelligence (CCI)
School of Computer Science and Informatics
De Montfort University
The Gateway, Leicester, LE1 9BH, UK.
Office: GH.4.54, Gateway House
Tel: +44(0)116 255 1551 ext 6757
Email: mmavrovouniotis at dmu.ac.uk


Personal Statement

I am currently a Postdoctoral Research Associate at the Centre of Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University. I work together with Prof. Shengxiang Yang for the EPSRC (EP/K001310/1) project on "Evolutionary Computation for Dynamic Optimisation in Network Environments".

I received a B.Sc in Computer Science at the University of Leicester and an M.Sc in Natural Computation at the University of Birmingham in 2008 and 2009, respectively. I completed a Ph.D in Computer Science at the University of Leicester in 2013.


Research Interests

  • Evolutionary Computation
  • Swarm Intelligence
  • Memetic Computing
  • Neural Networks
  • Combinatorial Optimization
  • Dynamic Optimization Problems

Dynamic Optimization Events


Publications

Journal Papers

  1. M. Mavrovouniotis, F. M. Müller, S. Yang. An ant colony optimization with local search for dynamic traveling salesman problems. IEEE Transactions on Cybernetics, accepted 16 April 2016. (DOI:10.1109/TCYB.2016.2556742, Source Code in C++)
  2. C. Li, T. T. Nguyen, M. Yang, M. Mavrovouniotis, and S. Yang. An adaptive multi-population framework for locating and tracking multiple optima. IEEE Transactions on Evolutionary Computation, accepted 18 November 2015. (DOI:10.1109/TEVC.2015.2504383)
  3. J. Eaton, S. Yang, M. Mavrovouniotis. Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays. Soft Computing, Springer Berlin Heidelberg, 2015. (DOI:10.1007/s00500-015-1924-x)
  4. M. Mavrovouniotis, S. Yang. Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Information Sciences, vol. 294, pp. 456-477, 2015. (DOI:10.1016/j.ins.2014.10.00, Source Code in C++).
  5. M. Mavrovouniotis, S. Yang. Training neural networks with ant colony optimization algorithms for pattern classification. Soft Computing, vol. 19, no. 6, pp. 1511-1522, Springer Berlin Heidelberg, 2014. (DOI:10.1007/s00500-014-1334-5).
  6. M. Mavrovouniotis, S. Yang. Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Applied Soft Computing, vol.13, no. 10, pp. 4023-4037, 2013. (DOI:10.1016/j.asoc.2013.05.022, Source Code in C++).
  7. M. Mavrovouniotis, S. Yang. A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Computing, vol. 15, no. 7, pp. 1405-1425, Springer-Verlag, 2011. (DOI:10.1007/s00500-010-0680-1).

Book Chapters

  1. M. Mavrovouniotis, S. Yang. Ant colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problem. S. Yang and X. Yao (eds.), Evolutionary Computation for Dynamic Optimization Problems, Studies in Computational Intelligence, vol. 490, Chapter 13, pp. 331-357, Springer Berlin Heidelberg, 2013. (DOI:10.1007/978-3-642-38416-5_13).
  2. M. Mavrovouniotis, S. Yang. Dynamic vehicle routing: A memetic ant colony optimization approach. A.S. Uyar, E. Ozcan and N. Urquhart (eds.) Automated Scheduling and Planning, Studies in Computational Intelligence, vol. 505, Chapter 9, pp. 283-301, Springer Berlin Heidelberg, 2013. (DOI:10.1007/978-3-642-39304-4_11).

Conference Papers

  1. M. Mavrovouniotis, S. Yang. Empirical study on the effect of population size on MAX-MIN Ant System in dynamic environments. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16), 2016
  2. M. Mavrovouniotis, S. Yang. Direct memory schemes for population-based incremental learning in cyclically changing environments. Applications of Evolutionary Computation, LNCS, vol. 9598, pp. 233-247, Springer, 2016. (PDF File, DOI:10.1007/978-3-319-31153-1_16). Nominated for Best Paper Award.
  3. M. Mavrovouniotis, S. Yang. Population-based incremental learning with immigrants schemes in changing environments. Proceedings of the 2015 IEEE Symposium on Compuational Intelligence in Dynamic and Uncertain Enviornments (CIDUE), pp. 1444-1451, 2015. (PDF File, DOI:10.1109/SSCI.2015.205)
  4. M. Mavrovouniotis, F. M. Müller, S. Yang. An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem. Proceedings of the 2015 Genetic and Evolutionary Computation Conference (GECCO15), pp. 49-56, ACM Press, 2015. (PDF File, DOI:10.1145/2739480.2754651). Nominated for Best Paper Award.
  5. M. Mavrovouniotis, F. Neri, S. Yang. An adaptive local search algorithm for real-valued dynamic optimization. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC'15), pp. 1388-1395, 2015. (PDF File, DOI:10.1109/CEC.2015.7257050).
  6. M. Mavrovouniotis, S. Yang. Applying ant colony optimization to dynamic binary-encoded problems. Applications of Evolutionary Computation, LNCS, vol. 9028, pp. 845-856, Springer International Publishing, 2015. (PDF File, DOI: 10.1007/978-3-319-16549-3_68).
  7. M. Mavrovouniotis, S. Yang, X. Yao. Multi-colony ant algorithms for the dynamic travelling salesman problem. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 9-16, 2014. (PDF File, DOI: 10.1109/CIDUE.2014.7007861).
  8. M. Mavrovouniotis, S. Yang. Ant colony optimization with self-adaptive evaporation rate in dynamic environments. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 47-54, 2014. (PDF File, DOI: 10.1109/CIDUE.2014.7007866).
  9. M. Mavrovouniotis, S. Yang. Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), pp. 1542-1549, 2014. (PDF File, DOI: 10.1109/CEC.2014.6900481).
  10. M. Mavrovouniotis, S. Yang. Elitism-based immigrants for ant colony optimization in dynamic environments: adapting the replacement rate. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), pp. 1752-1759, 2014. (PDF File, DOI: 10.1109/CEC.2014.6900482).
  11. M. Mavrovouniotis, S. Yang. Evolving neural networks using ant colony optimization with pheromone trail limits. Proceedings of the 2013 UK Workshop on Computational Intelligence (UKCI 2013), pp. 16-23, 2013. (PDF File, DOI:10.1109/UKCI.2013.6651282).
  12. M. Mavrovouniotis, S. Yang. Genetic algorithms with adaptive immigrants for dynamic environments. Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC'13), pp. 2130-2137, 2013. (PDF File, DOI:10.1109/CEC.2013.6557821).
  13. M. Mavrovouniotis, S. Yang. Adapting the pheromone evaporation rate in dynamic routing problems. Applications of Evolutionary Computation, LNCS, vol. 7835, pp. 606-615, Springer Berlin Heidelberg, 2013. (PDF File, DOI:10.1007/978-3-642-37192-9_61).
  14. M. Mavrovouniotis, S. Yang, X. Yao. A benchmark generator for dynamic permutation-encoded problems. Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN XII), LNCS, vol. 7492, pp. 508-517, Springer Berlin Heidelberg, 2012. (PDF File, DOI:10.1007/978-3-642-32964-7_51, Source Code in C++).
  15. M. Mavrovouniotis, S. Yang. Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem. Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC'12), pp. 2645-2652, 2012. (PDF File, DOI:10.1109/CEC.2012.6252885).
  16. M. Mavrovouniotis, S. Yang. Ant colony optimization with immigrants schemes for the dynamic vehicle routing problem. Applications of Evolutionary Computation, LNCS, vol. 7248, pp. 519-528, Springer Berlin Heidelberg, 2012. (PDF File, DOI:10.1007/978-3-642-29178-4_52).
  17. M. Mavrovouniotis, S. Yang. An immigrants scheme based on environmental information for ant colony optimization for the dynamic travelling salesman problem. Proceedings of the 10th International Conference on Artificial Evolution (EA-2011), LNCS, vol. 7401, pp. 1-12, Springer Berlin Heidelberg, 2012. (PDF File, DOI:10.1007/978-3-642-35533-2_1).
  18. M. Mavrovouniotis, S. Yang. An ant system with direct communication for the capacitated vehicle routing problem. Proceedings of the 2011 UK Workshop on Computational Intelligence (UKCI 2011), pp. 14-19, 2011. (PDF File).
  19. M. Mavrovouniotis, S. Yang. Memory-based immigrants for ant colony optimization in changing environments. Applications of Evolutionary Computation, LNCS, vol. 6624, pp. 324-333, Springer Berlin Heidelberg, 2011. (PDF File, DOI:10.1007/978-3-642-20525-5_33).
  20. M. Mavrovouniotis, S. Yang. Ant colony optimization with direct communication for the traveling salesman problem. Proceedings of the 2010 UK Workshop on Computational Intelligence (UKCI 2010), pp. 1-6, 2010. (PDF File, DOI:10.1109/UKCI.2010.5625608).
  21. M. Mavrovouniotis, S. Yang. Ant colony optimization with immigrants schemes in dynamic environments. Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN XI), LNCS, vol. 6238, pp. 371-380, Springer Berlin Heidelberg, 2010. (PDF File, DOI:10.1007/978-3-642-15871-1_38).

PhD Thesis

  1. M. Mavrovouniotis. Ant colony optimization in stationary and dynamic environments. Ph.D Thesis, University of Leicester: UK, 2013. (PDF File).

Author: Michalis Mavrovouniotis (mmavrovouniotis at dmu ac uk)
Created: March 2013.