Chaotic Quantum Genetic Algorithm and Its Application

In order to enhance the global and local search ability of genetic algorithm (GA) in solution space, an improved GA is introduced in this paper. First, the chaotic initialization is introduced into GA to improve its global search performance. Furthermore, improved quantum crossover is introduced to exchange information between different chromosomes, this action is useful for improving local search ability of GA. The performance of the proposed algorithm is evaluated by simulating a number of traveling salesman problems(TSP). Simulation results show that the proposed algorithm can avoid the premature convergence and has superior ability of searching the global optimal or near-optimum solutions.

[1]  Emilio Del-Moral-Hernandez,et al.  Chaotic Neural Networks , 2009, Encyclopedia of Artificial Intelligence.

[2]  Yudong Zhang,et al.  Face Pose Estimation by Chaotic Artificial Bee Colony , 2011 .

[3]  Rui Zhang,et al.  A Genetic Local Search Algorithm based on Insertion Neighborhood for the Job Shop Scheduling Problem , 2011 .

[4]  Chunguo Wu,et al.  Solving traveling salesman problems using generalized chromosome genetic algorithm , 2008 .

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

[6]  Yanchun Liang,et al.  A novel quantum swarm evolutionary algorithm and its applications , 2007, Neurocomputing.

[7]  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.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

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

[10]  Ying-Wu Chen,et al.  A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem , 2008, Eng. Appl. Artif. Intell..

[11]  Guanrong Chen,et al.  Simplex Sliding Mode Control for Nonlinear Uncertain Systems Via Chaos Optimization , 2005 .

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

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

[14]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[15]  El-Ghazali Talbi,et al.  A parallel hybrid genetic algorithm for protein structure prediction on the computational grid , 2007, Future Gener. Comput. Syst..

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