Abstract
The grid scheduling problem is concerted with some tasks assigning to a grid distributed system that the relative tasks have to exchange information on different grids. In the original particle swarm optimization (PSO) algorithm, particles search solutions in a continuous solution space. Since the solution space of the grid scheduling problem is discrete. This paper presents a discrete particle swarm optimization (PSO) that combines the simulated annealing (SA) method to solve the grid scheduling problems. The proposed discrete PSO uses a population of particles through a discrete space on the basis of information about each particle’s local best solution and global best solution of all particles. For generating the next solution of each particle, the SA is adopted into the discrete PSO. The objective is to minimize the maximum cost of the grid, which includes computing cost and communication cost. Simulation results show that the grid scheduling problem can be solved efficiently by the proposed method.
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References
Lee, L.T., Tao, D.F., Tsao, C.: An Adaptive Scheme for Predicting the Usage of Grid Resources. Comput. Electr. Eng. 33(1), 1–11 (2007)
Salman, A., Ahmad, I., Al-Madani, S.: Particle Swarm Optimization for Task Assignment Problem. Microprocessors and Microsystems 26, 363–371 (2002)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of State Calculations by Fast Computing Machines. J. of Chem. Phys. 21(6), 1087–1092 (1953)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Kennedy, J., Eberhard, R.C.: Particle Swarm Optimization. In: Proceedings IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Kuo, I.H., Horng, S.J., Kao, T.W., Lin, T.L., Lee, C.L., Terano, T., Pan, Y.: An Efficient Flow-shop Scheduling Algorithm based on a Hybrid Particle Swarm Optimization Model. Expert Syst. Appl. (2009) doi:10.1016/j.eswa.2008.08.054
Sha, D.Y., Hsu, C.Y.: A Hybrid Particle Swarm Optimization for Job Shop Scheduling Problem. Comput. Ind. Eng. 51, 791–808 (2006)
Bokhari, S.H.: Assignment Problems in Parallel and Distributed Computing. Kluwer Academic Publishers, Boston (1987)
Chaudhary, V., Aggarwal, J.K.: A Generalized Scheme for Mapping Parallel Algorithms. IEEE Trans. Parallel Distrib. Syst. 4, 328–346 (1993)
Norman, M.G., Thanisch, P.: Models of Machines and Computation for Mapping in Multicomputers. ACM Comput. Surv. 25, 263–302 (1993)
Liao, C.J., Tseng, C.T., Luarn, P.: A Discrete Version of Particle Swarm Optimization for Flowshop Scheduling Problems. Comput. Oper. Res. 34(10), 3099–3111 (2007)
Kennedy, J., Eberhard, R.C.: A Discrete Binary Version of the Particle Swarm Algorithm. In: Proceedings of IEEE Conference on Systems, Man, and Cybernetics, Piscataway, NJ, pp. 4104–4109 (1997)
Kashan, A.H., Karimi, B.: A Discrete Particle Swarm Optimization Algorithm for Scheduling Parallel Machines. Comput. Ind. Eng. 56(1), 216–223 (2009)
Kashan, A.H., Karimi, B., Jenabi, M.: A Hybrid Genetic Heuristic for Scheduling Parallel Batch Processing Machines with Arbitrary Job Sizes. Comput. Oper. Res. 35, 1084–1098 (2008)
Lee, W.C., Wu, C.C., Chen, P.: A Simulated Annealing Approach to Makespan Minimization on Identical Parallel Machines. Int. J. Adv. Manuf. Technol. 31, 328–334 (2006)
Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization for Integer Programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, pp. 1582–1587 (2002)
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Chen, RM., Shiau, DF., Lo, ST. (2009). Combined Discrete Particle Swarm Optimization and Simulated Annealing for Grid Computing Scheduling Problem. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_26
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DOI: https://doi.org/10.1007/978-3-642-04020-7_26
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