Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6024 LNCS, Issue PART 1, 2010, Pages 562-571

  Cheng, Hui ^a   Yang, Shengxiang ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; GRAPH THEORY; MESH NETWORKING; PARTICLE SWARM OPTIMIZATION (PSO); ROUTING ALGORITHMS;

DYNAMIC OPTIMIZATION PROBLEM (DOP); DYNAMIC SHORTEST PATH; ENVIRONMENTAL CHANGE; GENETIC ALGORITHM (GAS); INTELLIGENT OPTIMIZATION TECHNIQUE; MOBILE WIRELESS NETWORK; MULTI-POPULATION GENETIC ALGORITHM; WIRELESS COMMUNICATIONS;

MOBILE AD HOC NETWORKS;

EID: 77952332349     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12239-2_58     Document Type: Conference Paper

References (15)

1. Ali, M.K., Kamoun, F.: Neural networks for shortest path computation and routing in computer networks. IEEE Trans. on Neural Networks 4(6), 941-954 (1993)
2. Ahn, C.W., Ramakrishna, R.S., Kang, C.G., Choi, I.C.: Shortest path routing algorithm using hopfield neural network. Electronics Letters 37(19), 1176-1178 (2001)
3. Ahn, C.W., Ramakrishna, R.S.: A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans. on Evol. Comput. 6(6), 566-579 (2002)
4. Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Proc. 4th Int. Conf. on Adaptive Computing in Design and Manufacture, pp. 299-308 (2000)
5. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137-144 (1992)
6. Lee, S., Soak, S., Kim, K., Park, H., Jeon, M.: Statistical properties analysis of real world tournament selection in genetic algorithms. Applied Intell. 28(2), 195-205 (2008)
7. Mohemmed, A.W., Sahoo, N.C., Geok, T.K.: Solving shortest path problem using particle swarm optimization. Applied Soft Comput. 8(4), 1643-1653 (2008)
8. Oh, S., Ahn, C., Ramakrishna, R.: A genetic-inspired multicast routing optimization algorithm with bandwidth and end-to-end delay constraints. In: Proc. 13th Int. Conf. on Neural Information Processing, pp. 807-816 (2006)
9. Oppacher, F., Wineberg, M.: The shifting balance genetic algorithm: improving the GA in a dynamic environment. In: Proc. Genetic and Evol. Comput. Conf., pp. 504-510 (1999)
10. Parsa, M., Zhu, Q., Garcia-Luna-Aceves, J.: An iterative algorithm for delay-constrained minimum-cost multicasting. IEEE/ACM Trans. on Networking 6(4), 461-474 (1998)
11. Perkins, C.E. (ed.): Ad Hoc Networking. Addison-Wesley, London (2001)
12. Tsutsui, S., Fujimoto, Y., Ghosh, A.: Forking genetic algorithms: GAs with search space division schemes. Evol. Comput. 5(1), 61-80 (1997)
13. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815-834 (2005)
14. Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. on Evol. Comput. 12(5), 542-561 (2008)
15. Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16(3), 385-416 (2008)