Abstract
This paper proposes an improved particle swarm optimization algorithm with simulated annealing (IPSO-SA) for the task scheduling problem of cloud data center. The algorithm uses Tent chaotic mapping to make the initial population more evenly distributed. Second, a non-convex function is constructed to adaptively and decreasingly change the inertia weights to adjust the optimization-seeking ability of the particles in different iteration periods. Finally, the Metropolis criterion in SA is used to generate perturbed particles, combined with an modified equation for updating particles to avoid premature particle convergence. Comparative experimental results show that the IPSO-SA algorithm improves 13.8% in convergence accuracy over the standard PSO algorithm. The respective improvements over the other two modified PSO are 15.2% and 9.1%.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4. IEEE (1995)
Garnier, S., Gautrais, J., Theraulaz, G.: The biological principles of swarm intelligence. Swarm Intell. 1, 3–31 (2007)
Eltamaly, A.M.: A novel strategy for optimal PSO control parameters determination for PV energy systems. Sustainability 13(2), 1008 (2021)
Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm Evol. Comput. 41, 20–35 (2018)
Shami, T.M., et al.: Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031–10061 (2022)
Li, M., et al.: A multi-information fusion “triple variables with iteration’’ inertia weight PSO algorithm and its application. Appl. Soft Comput. 84, 105677 (2019)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3. IEEE (1999)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)
Kao, Y.-T., Zahara, E.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008)
Niu, B., et al.: MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl. Math. Comput. 185(2), 1050–1062 (2007)
Alguliyev, R.M., Imamverdiyev, Y.N., Abdullayeva, F.J.: PSO-based load balancing method in cloud computing. Autom. Control. Comput. Sci. 53, 45–55 (2019)
Parsopoulos, K.E., et al.: Improving particle swarm optimizer by function “stretching”. In: Hadjisavvas, N., Pardalos, P.M. (eds.) Advances in Convex Analysis and Global Optimization Nonconvex Optimization and Its Applications, vol. 54, pp. 445–457. Springer, Boston (2001). https://doi.org/10.1007/978-1-4613-0279-7_28. Ch 3
Van Laarhoven, P.J.M., et al.: Simulated Annealing. Springer, Dordrecht (1987). https://doi.org/10.1007/978-94-015-7744-1
Calheiros, R.N., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
Lei, K., Qiu, Y., He, Y.: A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics. IEEE (2006)
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Bi, Y., Ni, W., Liu, Y., Lai, L., Zhou, X. (2024). Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_21
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DOI: https://doi.org/10.1007/978-981-99-8067-3_21
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