Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

Zhe Lian

doi:10.4028/www.scientific.net/AMR.143-144.547

Paper Titles

A Novel Empirical Mode Decomposition Denoising Scheme
p.527

Perceptually Optimized UDP-Lite Protocol for 3D Model Streaming among Lossy Network
p.533

An Improved Algorithm for TSP Problem Solving with Hopfield Neural Networks
p.538

Design of the Thermoelectric Generator
p.543

Immune-Inspired Quantum Genetic Optimization Algorithm and its Application
p.547

A Novel Heuristic Routing Algorithm for Peer-to-Peer Network
p.552

Enterprise Search Services and Application of Security Database System
p.557

Study of Athletic Trauma in College Physical Education
p.562

The Insulator’s Pollution Raining Flashover Forecast under GARCH-Based Forecast of Rainstorm Disasters1
p.566

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 143-144Immune-Inspired Quantum Genetic Optimization...

Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

Abstract:

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic Algorithm (GA) on performance.

You might also be interested in these eBooks

Smart Materials and Intelligent Systems, SMIS2010

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Pages:

547-551

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.143-144.547

Citation:

Cite this paper

Online since:

October 2010

Authors:

Zhe Lian

Keywords:

Clonal Selection Algorithm, Genetic Algorithm (GA), Immune-Inspired Quantum Genetic Optimization Algorithm, Quantum Algorithm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L.N. de Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, London, UK, (2002).

Google Scholar

[2] D. Dasgupta, Advances in artificial immune systems, IEEE Computational Intelligence Magazine 2006, 1 (4) : 40-49.

Google Scholar

[3] X. Wang, X.Z. Gao, S.J. Ovaska, Artificial immune optimization methods and applications-a survey, in: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, The Hague, The Netherlands, October 2004, pp.3415-3420.

DOI: 10.1109/icsmc.2004.1400870

Google Scholar

[4] L.N. de Castro, F.J. von Zuben, Learning and optimization using the clonal selection principle, IEEE Transactions on Evolutionary Computation , 2002, 6 (3): 239-251.

DOI: 10.1109/tevc.2002.1011539

Google Scholar

[5] R. Storn, K. Price, Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization 1997, 11 (7) : 341-359.

Google Scholar

[6] G.L. Ada, G. Nossal, The clonal selection theory, Sci. Am. 257 1987, pp.50-57.

Google Scholar

[7] L.N. De Castro, F.J. Von Zuben, Learning and optimization using the clonal selection principle, IEEE Trans. Evol. Computer., 2002, 6 (4) : 239-251.

DOI: 10.1109/tevc.2002.1011539

Google Scholar

[8] C.A. Coello Coello, N.C. Cortes, Solving multiobjective optimization problems using an artificial immune system, Genet. Program. Evolvable Mach. 6 (2005) 163-190.

DOI: 10.1007/s10710-005-6164-x

Google Scholar

[9] F. Campelo, F.G. Guimaraes, H. Igarashi, J.A. Ramirez, A clonal selection algorithm for optimization in electromagnetics, IEEE Trans. Magn. 2005, 41 (5) : 1736-1739.

DOI: 10.1109/tmag.2005.846043

Google Scholar