Effective Algorithm of Simulated Annealing for the Symmetric Traveling Salesman Problem

2018 ◽  
pp. 348-359
Author(s):  
Mariusz Makuchowski
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Effective Algorithm ◽  
Symmetric Traveling Salesman Problem

scholarly journals Numerical Approach of Symmetric Traveling Salesman Problem Using Simulated Annealing

2021 ◽  
Vol 5 (6) ◽  
pp. 1090-1098
Author(s):  
I Iryanto ◽  
Putu Harry Gunawan
Keyword(s):  
Simulated Annealing ◽  
Standard Deviation ◽  
Traveling Salesman Problem ◽  
Relative Error ◽  
Numerical Approach ◽  
Traveling Salesman ◽  
Computational Time ◽  
Temperature Ratio ◽  
Research Papers ◽  
Symmetric Traveling Salesman Problem

The aim of this paper is to elaborate the performance of Simulated Annealing (SA) algorithm for solving traveling salesmen problems. In this paper, SA algorithm is modified by using the interaction between outer and inner loop of algorithm. This algorithm produces low standard deviation and fast computational time compared with benchmark algorithms from several research papers. Here SA uses a certain probability as indicator for finding the best and worse solution. Moreover, the strategy of SA as cooling to temperature ratio is still given. Thirteen benchmark cases and thirteen square grid symmetric TSP are used to see the performance of the SA algorithm. It is shown that the SA algorithm has promising results in finding the best solution of the benchmark cases and the squared grid TSP with relative error 0 - 7.06% and 0 – 3.31%, respectively. Further, the SA algorithm also has good performance compared with the well-known metaheuristic algorithms in references.

scholarly journals Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 884
Author(s):  
Petr Stodola ◽  
Karel Michenka ◽  
Jan Nohel ◽  
Marian Rybanský
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Use Of Knowledge

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

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