Study of Parametric Relation in Ant Colony Optimization Approach to Traveling Salesman Problem

Presetting control parameters of algorithms are important to ant colony optimization (ACO). This paper presents an investigation into the relationship of algorithms performance and the different control parameter settings. Two tour building methods are used in this paper including the max probability selection and the roulette wheel selection. Four parameters are used, which are two control parameters of transition probability α andβ, pheromone decrease factor ρ, and proportion factor q0 in building methods. By simulated result analysis, the parameter selection rule will be given.

[1]  De-Shuang Huang,et al.  Determining the centers of radial basis probabilistic neural networks by recursive orthogonal least square algorithms , 2005, Appl. Math. Comput..

[2]  Jun Zhang,et al.  Time Series Prediction Using Lyapunov Exponents In Embedding Phase Space , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

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

[4]  Jun Zhang,et al.  Time Series Prediction Using Lyapunov Exponents In Embedding Phase Space , 1998, SMC.

[5]  De-Shuang Huang,et al.  Zeroing polynomials using modified constrained neural network approach , 2005, IEEE Transactions on Neural Networks.

[6]  De-Shuang Huang,et al.  A constructive approach for finding arbitrary roots of polynomials by neural networks , 2004, IEEE Transactions on Neural Networks.

[7]  Jun Zhang,et al.  Implementation of an Ant Colony Optimization technique for job shop scheduling problem , 2006 .

[8]  Angus R. Simpson,et al.  Parametric study for an ant algorithm applied to water distribution system optimization , 2005, IEEE Transactions on Evolutionary Computation.

[9]  Luca Maria Gambardella,et al.  Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem , 1995, ICML.

[10]  Marco Dorigo,et al.  An Investigation of some Properties of an "Ant Algorithm" , 1992, PPSN.

[11]  Marco Dorigo,et al.  Ant system for Job-shop Scheduling , 1994 .

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Marco Dorigo,et al.  Search bias in ant colony optimization: on the role of competition-balanced systems , 2005, IEEE Transactions on Evolutionary Computation.

[14]  De-Shuang Huang,et al.  Dilation method for finding close roots of polynomials based on constrained learning neural networks , 2003 .

[15]  S. Kannan,et al.  Application and comparison of metaheuristic techniques to generation expansion planning problem , 2005, IEEE Transactions on Power Systems.

[16]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[17]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .