A Hybrid Algorithm to Solve Traveling Salesman Problem

The TSP problem is a typical one in the field of combinatorial optimization. After study other researchers’ related works, this paper presents a hybrid algorithm based on simulated annealing, ant colony and genetic in reference to previous research, in order to improve computing performance. Algorithms of this paper are used for solving traveling salesman problem, and the simulation contrast test results show that the algorithm has better convergence speed and optimal results; it also shows that the algorithm is feasible and effective.

[1]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[2]  S. Vassiliadis,et al.  HYBRID NEWTON-RAPHSON GENETIC ALGORITHM FOR THE TRAVELING SALESMAN PROBLEM , 1995 .

[3]  T. Stützle,et al.  MAX-MIN Ant System and local search for the traveling salesman problem , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[4]  Wu Qing AN ANT COLONY ALGORITHM WITH MUTATION FEATURES , 1999 .

[5]  TaeChoong Chung,et al.  An effective dynamic weighted rule for ant colony system optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Thomas Stützle,et al.  Guest editorial: special section on ant colony optimization , 2002, IEEE Trans. Evol. Comput..

[7]  Shuzhi Sam Ge,et al.  On parameter settings of Hopfield networks applied to traveling salesman problems , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Gao Shang Solving Traveling Salesman Problem by Ant Colony Optimization Genetic Hybrid Algorithm , 2009 .

[9]  Liu Guo-dong Solution of TSP problem based on hybrid genetic simulated annealing algorithm , 2010 .

[10]  Wang Minyi,et al.  Analysis on convergence of mean-shift and angle of continuous mean-shift vector , 2010 .

[11]  Moe Key,et al.  Improved simulated annealing algorithm for TSP , 2010 .

[12]  Guo Yan-yan Using Ant Colony Algorithm and Simulated Annealing Parallel Algorithm to Solve Traveling Salesman Problem , 2010 .