Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

Clonal Selection Algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based Clonal Selection Algorithm.

[1]  Uwe Aickelin,et al.  Danger Theory: The Link between AIS and IDS? , 2003, ICARIS.

[2]  Xin Yao,et al.  Recent Advances in Evolutionary Computation , 2006, Journal of Computer Science and Technology.

[3]  Hajime Kita,et al.  A genetic solution for the traveling salesman problem by means of a thermodynamical selection rule , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Zheng Tang,et al.  An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[6]  Nikos A. Aspragathos,et al.  Optimal robot task scheduling based on genetic algorithms , 2005 .

[7]  J Faro,et al.  Further studies on the problem of immune network modelling. , 1997, Journal of theoretical biology.

[8]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[9]  Fabio A. González,et al.  Anomaly Detection Using Real-Valued Negative Selection , 2003, Genetic Programming and Evolvable Machines.

[10]  H.,et al.  The Immune System as a Model for Pattern Recognition and Classification , 1999 .

[11]  Alper Döyen,et al.  A new approach to solve hybrid flow shop scheduling problems by artificial immune system , 2004, Future Gener. Comput. Syst..

[12]  Rogério de Lemos,et al.  Negative Selection: How to Generate Detectors , 2002 .

[13]  Dipankar Dasgupta,et al.  Anomaly detection in multidimensional data using negative selection algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  D. Nemazee,et al.  Receptor editing in self-reactive bone marrow B cells , 1993, The Journal of experimental medicine.

[15]  Uwe Aickelin,et al.  The Danger Theory and Its Application to Artificial Immune Systems , 2008, ArXiv.

[16]  Eugene L. Lawler,et al.  A Guided Tour of Combinatorial Optimization , 1985 .

[17]  Ajit Narayanan,et al.  Quantum-inspired genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  A. Perelson Immune Network Theory , 1989, Immunological reviews.

[19]  Liangpei Zhang,et al.  Multispectral remote sensing image classification based on simulated annealing clonal selection algorithm , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[20]  D. Fogel Evolutionary algorithms in theory and practice , 1997, Complex..

[21]  F. Burnet The clonal selection theory of acquired immunity , 1959 .

[22]  D. Nemazee,et al.  The scope of receptor editing and its association with autoimmunity. , 2004, Current opinion in immunology.

[23]  Tim Hendtlass,et al.  Dynamic Ant Colony Optimisation , 2005, Applied Intelligence.

[24]  Li Yang Quantum Clonal Algorithm for Multicast Routing Problem , 2007 .

[25]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[26]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[27]  R. Feynman Simulating physics with computers , 1999 .

[28]  R. Pelanda,et al.  Receptor editing for better or for worse. , 2006, Current opinion in immunology.

[29]  P. Benioff The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines , 1980 .

[30]  Zheng Tang,et al.  A Novel Clonal Selection Algorithm and its Application , 2007, 2008 International Conference on Apperceiving Computing and Intelligence Analysis.

[31]  S. Camper,et al.  Receptor editing: an approach by autoreactive B cells to escape tolerance , 1993, The Journal of experimental medicine.

[32]  E. Ahmed,et al.  Immune-Motivated Optimization , 2002 .