A PSO-Optimized Nash Equilibrium-Based Task Scheduling Algorithm for Wireless Sensor Network - Frontiers in Internet Technologies - page 73

3. Liu, M., Li, H.H., Shen, Y.: Research on task allocation technique for aerial target tracking
based on wireless sensor network. Journal of Astronautics 28(4), 960-965 (2007)
4. Chen, G.L., Guo, W.Z., Chen, Y.Z.: Research on dynamic alliance of task allocation and
its algorithm in wireless sensor network. Journal on Communication 30(11), 48-55 (2009)
5. Guo, W.Z., Xiong, N.X., Chao, H.C., et al.: Design and Analysis of Self-Adapted Task
Scheduling Strategies in Wireless Sensor Networks. Sensors 11(7), 6533-6554 (2011)
6. Maynard, S.J.: Evolution and the Theory of Games. Cambridge University Press,
Cambridge (1982)
7. Thomas, R.: Genetic Algorithm Learning and Evolutionary Games. Journal of Economic
Dynamics & Control 25, 1019-1037 (2001)
8. Chen, S.J., Sun, Y.G., Wu, Z.G.: A Genetic Algorithm to Acquire the Nash Equilibrium.
System Engineering 19(5), 67-70 (2001)
9. Daskalakis, C., Goldberg, P.W., Papadimitriou, C.H.: The complexity of computing Nash
equilibrium. In: STOC 2006, pp. 71-78. ACM press, Seattle (2005)
10. Thomas, R.: Genetic algorithm learning and evolutionary games. Journal of Economic
Dynamics & Control 25(6), 1019-1037 (2001)
11. Kan, Y., Wang, R.C.: Nash Equilibrium Based Task Scheduling Algorithm of Multi-
schedulers in Grid Computing. Acta Electronica Sinica 37(2), 329-333 (2009)
12. Zhang, G.F., Jiang, J.G., Xia, N., Su, Z.P.: Solutions of complicated coalition generation
based on discrete particle swarm optimization. Acta Electronica Sinica 35(2), 323-327
(2007)
13. Wu, Q., Rao, N.S., Barhen, J.: On computing mobile agent routes for data fusion in distri-
buted sensor networks. IEEE Trans. on Knowledge and Data Engineering 16(6), 740-753
(2004)
14. Garey, M.R., Johnson, D.S.: “Strong” NP-Completeness Results: Motivation, Examples,
and Implications. Journal of the Association for Computing Machinery 25(3), 499-508
(1978)
15. Mezura-Montes, E., Coello, C.A.C.: A simple multi-membered evolution strategy to save
constrained optimization problems. IEEE Transactions on Evolutionary Computation 9(1),
1-17 (2005)