University of Leicester

computer science

Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications


Publications


Journal Papers

  1. H. Cheng and S. Yang. Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods. Applied Soft Computing, accepted in June 2010. Elsevier (Source Code).
  2. S. Yang and C. Li. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation, accepted in November 2009. IEEE Press (DOI: 10.1109/TEVC.2010.2046667 and Source Code).
  3. S. Yang and S. N. Jat. Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, published online first: 3 June 2010. IEEE Press (DOI: 10.1109/TSMCC.2010.2049200).
  4. L. Liu, S. Yang, and D. Wang. Particle swarm optimization with composite particles in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, published online first: 5 April 2010. IEEE Press (DOI: 10.1109/TSMCB.2010.2043527).
  5. X. Peng, X. Gao, and S. Yang. Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Computing, published online first: 11 February 2010. Springer (DOI: 10.1007/s00500-010-0547-5).
  6. H. Wang, S. Yang, W. H. Ip, and D. Wang. A memetic algorithm based on particle swarm optimization for dynamic optimization problems. Natural Computing, published online first: 6 January 2010. Springer (DOI: 10.1007/s11047-009-9176-2).
  7. F. Neri and S. Yang. Guest editorial: Memetic computing in the presence of uncertainties. Memetic Computing, 2(2): 85-86, June 2010. Springer (DOI: 10.1007/s12293-010-0033-8).
  8. H. Cheng and S. Yang. Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Engineering Applications of Artificial Intelligence, 23(5): 806-819, August 2010. Elsevier (DOI: 10.1016/j.engappai.2010.01.021 and Source Code Part 1 and Source Code Part 2).
  9. H. Cheng, X. Wang, S. Yang, M. Huang, and J. Cao. QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms. Journal of Network and Computer Applications, 33(4): 512-522, July 2010. Elsevier (DOI: 10.1016/j.jnca.2010.01.001).
  10. S. Yang, H. Cheng, and F. Wang. Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 40(1): 52-63, January 2010. IEEE Press (DOI: 10.1109/TSMCC.2009.2023676 and Source Code).
  11. H. Wang, S. Yang, W. H. Ip, and D. Wang. Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6): 1348-1361, December 2009. IEEE Press (DOI: 10.1109/TSMCB.2009.2015281).
  12. H. Richter and S. Yang. Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Computing, 13(12): 1163-1173, October 2009. Springer (DOI: 10.1007/s00500-009-0420-6).
  13. S. Yang, D. Wang, T. Chai, and G. Kendall. An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1): 17-38, February 2010. Springer (DOI: 10.1007/s10951-009-0106-z).
  14. H. Wang, D. Wang, and S. Yang. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 13(8-9): 763-780, July 2009. Springer (DOI: 10.1007/s00500-008-0347-3).
  15. H. Cheng, J. Cao, X. Wang, S. K. Das, and S. Yang. Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility. Wireless Communications and Mobile Computing, 9(6): 759-771, June 2009. John Wiley & Sons, Ltd (DOI: 10.1002/wcm.627).
  16. H. Cheng, X. Wang, S. Yang, and M. Huang. A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks. Applied Soft Computing, 9(2): 677-684, March 2009. Elsevier (DOI: 10.1016/j.asoc.2008.09.008).).
  17. S. Yang and X. Yao. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5): 542-562, October 2008. IEEE Press (DOI: 10.1109/TEVC.2007.913070).
  18. S. Yang. Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evolutionary Computation, 16(3): 385-416, Fall 2008. The MIT Press (DOI: 10.1162/evco.2008.16.3.385).
  19. R. Tinos and S. Yang. A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines, 8(3): 255-286, September 2007. Springer (DOI: 10.1007/s10710-007-9024-z).
  20. S. Yang and R. Tinos. A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, 4(3): 243-254, July 2007. Springer (DOI: 10.1007/s11633-007-0243-9).

Conference Papers

  1. R. Tinos and S. Yang. Dynamic evolutionary optimization: an analysis based on the dynamical system approach. Proceedings of the 11th International Conference on Parallel Problems Solving from Nature, 2010.
  2. M. Mavrovouniotis and S. Yang. Ant colony optimization with immigrants schemes for changing environments. Proceedings of the 11th International Conference on Parallel Problems Solving from Nature, 2010. Springer.
  3. R. Tinos and S. Yang. Evolution strategies with q-Gaussian mutation for dynamic optimization problems. Proceedings of the 11th Brazilian Syposium on Artificial Neural Network, 2010.
  4. C. Li and S. Yang. Adaptive learning particle swarm optimizer--II for function optimization. Proceedings of the 2010 IEEE Congress on Evolutionary Computation, 2010.
  5. S. Arshad and S. Yang. A hybrid genetic algorithm and inver over approach for the travelling salesman problem. Proceedings of the 2010 IEEE Congress on Evolutionary Computation, 2010.
  6. I. Korejo, S. Yang, and C. Li. A directed mutation operator for real coded genetic algorithms. EvoApplications 2010: Applications of Evolutionary Computing, Part I, LNCS 6024, pp. 491-500, 2010. Springer.
  7. H. Cheng and S. Yang. Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. EvoApplications 2010: Applications of Evolutionary Computing, Part I, LNCS 6024, pp. 562-571, 2010. Springer.
  8. I. Korejo, S. Yang, and C. Li. A comparative study of adaptive mutation operators for metaheuristics. Proceedings of the 8th Metaheuristic International Conference, 2009.
  9. S. N. Jat and S. Yang. A guided search genetic algorithm for the university course timetabling problem. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), pp. 180-191, 2009.
  10. S. Arshad, S. Yang, and C. Li. A sequence based genetic algorithm with local search for the travelling salesman problem. Proceedings of the 2009 UK Workshop on Computational Intelligence, pp. 98-105, 2009.
  11. I. Korejo, S. Yang, and C. Li. A comparative study of adaptive mutation operators for metaheuristics. Proceedings of the 8th Metaheuristic International Conference, 2009.
  12. H. Cheng and S. Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using tabu search. Proceedings of the 5th International Conference on Natural Computation, 2009. IEEE Press.
  13. S. N. Jat and S. Yang. A guided search genetic algorithm for the university course timetabling problem. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 2009.
  14. C. Li and S. Yang. An adaptive learning particle swarm optimizer for function optimization. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 381-388, 2009. IEEE Press.
  15. C. Li and S. Yang. A clustering particle swarm optimizer for dynamic optimization. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 439-446, 2009. IEEE Press.
  16. S. Yang and H. Richter. Hyper-learning for population-based incremental learning in dynamic environments. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 682-689, 2009. IEEE Press
  17. H. Cheng and S. Yang. Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 3135-3140, 2009. IEEE Press.
  18. L. Liu, D. Wang, and S. Yang. An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems. EvoWorkshops 2009: Applications of Evolutionary Computing, LNCS 5484, pp. 725-734, 2009. Springer (DOI: 10.1007/978-3-642-01129-0_82).
  19. H. Cheng and S. Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using simulated annealing. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 370-380, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_38).
  20. C. Li and S. Yang. A generalized approach to construct benchmark problems for dynamic optimization. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 391-400, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_40).
  21. C. Li and S. Yang. An island based hybrid evolutionary algorithm for optimization. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 180-189, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_19).
  22. S. N. Jat and S. Yang. A memetic algorithm for the university course timetabling problem. Proceedings of the 20th IEEE Int. Conf. on Tools with Artificial Intelligence, vol. 1, pp. 427-433, 2008. IEEE Press (DOI: 10.1109/ICTAI.2008.126).
  23. C. Li and S. Yang. Fast multi-swarm optimization for dynamic optimization problems. Proceedings of the 4th International Conference on Natural Computation, vol. 7, pp. 624-628, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.313).
  24. H. Richter and S. Yang. Learning in abstract memory schemes for dynamic optimization. Proceedings of the 4th International Conference on Natural Computation, vol. 1, pp. 86-91, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.110).
  25. C. Ji, Y. Zhang, M. Tong, and S. Yang. Particle filter with swarm move for optimization. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature, LNCS 5199, pp. 909-918, 2008. Springer (DOI: 10.1007/978-3-540-87700-4_90).
  26. H. Cheng, X. Wang, M. Huang, and S. Yang. A review of personal communications services. Proceedings of the 9th International Conference for Young Computer Scientists, pp. 616-621, 2008. IEEE Press (DOI: 10.1109/ICYCS.2008.191).
  27. H. Cheng and S. Yang. A Genetic-inspired joint multicast routing and channel assignment algorithm in wireless mesh networks. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 159-164, 2008.
  28. C. Li and S. Yang. An adaptive mutation operator for particle swarm optimization. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 165-170, 2008.
  29. R. Tinos and S. Yang. Evolutionary programming with q-Gaussian mutation for dynamic pptimization problems. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1823-1830, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631036)
  30. Y. Yan, H. Wang, D. Wang, S. Yang, and D. Z. Wang. A multi-agent based evolutionary algorithm in non-stationary environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 2972-2979, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631198)
  31. S. Yang and R. Tinos. Hyper-selection in dynamic environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 3184-3191, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631229).
  32. H. Richter and S. Yang. Memory based on abstraction for dynamic fitness functions. In Applications of Evolutionary Computing, LNCS 4974 , pp. 597-606, 2008. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-78761-7_65).
  33. L. Liu, D. Wang, and S. Yang. Compound particle swarm optimization in dynamic environments. In Applications of Evolutionary Computing, LNCS 4974, pp. 617-626, 2008. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-78761-7_67).
  34. R. Tinos and S. Yang. Self-adaptation of mutation distribution in evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 79-86, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424457).
  35. R. Tinos and S. Yang. Continuous dynamic problem generators for evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 236-243, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424477).

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Author: Shengxiang Yang (s.yang@mcs.le.ac.uk).
© University of Leicester. First created: Mon 14 May 2007. Last modified: 26th October 2009.
CS Web Maintainer. This document has been approved by the Head of Department.