A study of simulated annealing technique and its application to traveling salesman problem

 Author: Ho, Chi-leung Title: A study of simulated annealing technique and its application to traveling salesman problem Degree: M.Sc. Year: 1998 Subject: Simulated annealing (Mathematics)Traveling-salesman problem -- Data processingCombinatorial optimizationMathematical optimizationAlgorithmsHong Kong Polytechnic University -- Dissertations Department: Multi-disciplinary StudiesDept. of Management Pages: iv, 68 leaves : ill. ; 30 cm Language: English InnoPac Record: http://library.polyu.edu.hk/record=b1441861 URI: http://theses.lib.polyu.edu.hk/handle/200/1280 Abstract: Simulated Annealing is a global optimization method that distinguishes between different local optima. Starting from an initial point, the algorithm takes a step and function is evaluated. When minimizing a function, any downhill step is accepted and the process repeats from this new point. An uphill step may be accepted with certain probability. Thus, it can escape from local optima. This uphill decision is made by the Metropolis criteria. As the optimization process proceeds, the length of the steps decline and the algorithm closes in on the global optimum. Since the algorithm makes very few assumptions regarding the function to be optimized, it is quite robust with respect to non-quadratic functions. The degree of robustness can be adjusted by the user. The purpose of this project is to study the Simulated Annealing algorithm and apply it to, particularly, Traveling Salesman Problem. A computer program was written for this purpose. Moreover the effect of different parameters to the cooling schedule was also studied. In order to have the best result, most of the previous researches suggested that the initial temperature should be set high enough, the cooling process should be as slow and as long as possible. However the time required to obtaining the result will be increased substantially. In this project, different parameter sets were tested in order to achieve a near-optimal solution at the shortest time.

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