Abstract
A hybrid algorithm that integrates ITO algorithm with PSO algorithm is proposed for solving numerical optimization problem in this paper. We design a strategy that exert a synchronized wave process to move and wave operator so as to improve the ability of finding global optimization of the proposed algorithm. Specially, we employ the mechanism of PSO algorithm, thus mapping the wave coefficient to C 1 and the move coefficient to C 21 where C 1 and C 2 are two parameters in PSO algorithm, while the inertia weight decreases linearly with temperature synchronously, finally we use the individual optimization to exert an additional wave process to the population, which lead the particles to jump out of the local optimal solution and move in the direction of global optimal solution. Experiments have been performed on several benchmark test functions, which usually are intractable to achieve good solutions , then comparison is conducted to show the performance difference to standard PSO and ITO. The experimental results demonstrate that the proposed algorithm is feasible, possesses fast convergence speed, strong robustness and stability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4(2), pp. 1942–1948 (1995)
Dong, W.Y., Zhang, W.S., Yu, R.G.: Convergence and Runtime Analysis of ITO Algorithm for One Class of Combinatorial Optimization. Chinese Journal of Computers 34(4), 636–646 (2011) (in Chinese)
Dong, W., Lei, M., Yu, R.: A New Evolutionary Algorithms for Global Numerical Optimization Based on Ito Process. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds.) ISICA 2010. CCIS, vol. 107, pp. 57–67. Springer, Heidelberg (2010)
Dong, W.Y., et al.: BBOB-benchmarking: A new evolutionary algorithms inspired by ITO process for noiseless function tested. Journal of Computational Information Systems, 2195–2203 (2011)
Dong, W.Y.: Time Series Modeling Based on ITO Algorithm. In: Proc. Int. Conf. on Natural Computation, pp. 398–402 (2007)
Dong, W., Yu, R., Lei, M.: Merging the Ranking and Selection into ITO Algorithm for Simulation Optimization. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds.) ISICA 2010. CCIS, vol. 107, pp. 87–96. Springer, Heidelberg (2010)
Dong, W.Y.: Simulation Optimization Based on the Hypothesis Testing and ITO Process. In: Third International Conference on Natural Computation, pp. 1210–1221 (2007)
Yi, Y.F., Cai, Y.L., Dong, W.Y., et al.: Improved ITO Algorithm for Solving the CVRP. Computer Science 40(5), 213–216 (2013) (in Chinese)
Dong, W., Zhang, D., Weicheng, Z., Leng, J.: The Simulation Optimization Algorithm Based on the Ito Process. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. CCIS, vol. 2, pp. 563–573. Springer, Heidelberg (2007)
Dong, W., Zhang, D., Li, Y.: The Multi-objective ITO Algorithms. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 53–61. Springer, Heidelberg (2007)
Hung, H., Qin, H., Hao, Z.F.: Example-based Learning Particle Swarm Optimization for Continuous Optimization Sciences. Information Sciences 182(1), 125–138 (2012)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: The 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995)
Ling, S.H., Iu, H.H.C., Chan, K.Y., et al.: Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38(3), 743–764 (2008)
Corana, A., Marchesi, M., Martini, C., et al.: Minimizing multimode functions of continuous variables with the Simulated Annealing Algorithm. ACM Transactions on Mathematical Software 13(3), 262–280 (1987)
He, Y.C., Zhang, C.J., Wang, P.H., et al.: Comparison between Particle Swarm Optimization and Guo Tao algorithm on function optimization problems. Computer Engineering and Applications 43(11), 100–103 (2007) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yi, Y., Lin, X., Sheng, K., Jiang, L., Dong, W., Cai, Y. (2014). Hybrid ITO Algorithm for Solving Numerical Optimization Problem. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-09333-8_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
eBook Packages: Computer ScienceComputer Science (R0)