Abstract
In order to cope with the multidimensional non-linear optimization problems which involved a great number of discrete variables and continuous variables, a self-organizing learning algorithm (SOLA) was proposed in this paper, in which the parallel search strategy of genetic algorithm(GA) and the serial search strategy of simulated annealing (SA) were involved. Additionally, the learning principle of particle swarm optimization(PSO) and the tabu search strategy were adopted into the SOLA, wherein the integrated frame work was different from traditional optimization methods and the interactive learning strategy was involved in the process of random searching. SOLA was divided into two handling courses: self-learning and interdependent-learning. The local optimal solution would be achieved through self-learning in the process of local searching and the global optimal solution would be achieved via the interdependent learning based on the information sharing mechanism. The search strategies and controlled parameters of SOLA were adaptively fixed according to the feedback information from interactive learning with the environments thus SOLA is self-organizing and intelligent. Experiments for the multidimensional testbed functions showed that SOLA was far superior to traditional optimization methods at the robustness and the global search capability while the solution space ranged from low-dimensional space to the high-dimensional space.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Glover, F.: Tabu search-PartI. ORSA Journal on Computing 1(3), 190–206 (1989)
Glover, F.: Tabu search-PartII. ORSA Journal on Computing 2(1), 4–32 (1990)
Metroplis, N., Rosenbluth, A., Rosenbluth, M., et al.: Equation of state calculation by fast computing machines. Journal of Cherimal Physics 21, 1087–1092 (1953)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated Annealing. Science 220, 671–680 (1983)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization: Proc. IEEE International Conference on Neural Network, pp. 1942–1948. IEEE Service Center, Riscataway (1995)
Eberhart, R.C., Kennedy, J.: A New optimizer using particle swarm theory. In: Proc. on 6th International Symposium on Micromachine & Human Science, pp. 39–43. IEEE Service Center, Riscataway (1995)
Sun, Y., Sun, Z.: Application of Hybrid Niche Genetic Simulated Annealing Algorithm to Dynamic Traffic Assignment. Journal of Highway & Transportation Research & development 25(5), 95–99 (2008) (in Chinese)
Wu, H.-y., Chang, B.-g., Zhu, C.-c., Liu, J.-h.: A Multigroup Parallel Genetic Algorithm Based on Simulated Annealing Method. Journal of Software 11(4), 416–420 (2000) (in chinese)
Fan, Y.-m., Yu, J.-j., Fang, Z.-m.: Hybrid Genetic Simulated Annealing Algorithm Based on Niching for QoS multicast routing. Journal of Communications 29(5), 65–71 (2008) (in chinese)
Sun, Y.-F., Zhang, C.-K., Gao, J.-G., Deng, F.-Q.: For Constrained Non-Linear Programming:Chaotic Parallel Genetic Algorithm with Feedback. Chinese Journal of Computers 30(3), 424–430 (2007) (in Chinese)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trasaction on Evolutionary Computation 8(3), 240–255 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, C.H., Xie, A.S., Zhao, B.H. (2010). Self-organizing Learning Algorithm for Multidimensional Non-linear Optimization Applications. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-16167-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16166-7
Online ISBN: 978-3-642-16167-4
eBook Packages: Computer ScienceComputer Science (R0)