A quantitative analysis of the simulated annealing algorithm: A case study for the traveling salesman problem

1988 ◽  
Vol 50 (1-2) ◽  
pp. 187-206 ◽  
Author(s):  
Emile H. L. Aarts ◽  
Jan H. M. Korst ◽  
Peter J. M. van Laarhoven
Keyword(s):  
Simulated Annealing ◽  
Quantitative Analysis ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Traveling Salesman ◽  
Annealing Algorithm ◽  
The Traveling Salesman Problem

Optimization Models and Heuristic Method Based on Simulated Annealing Strategy for Traveling Salesman Problem

2010 ◽  
Vol 34-35 ◽  
pp. 1180-1184
Author(s):  
Xu Hao
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Heuristic Method ◽  
Theoretical Computer Science ◽  
Traveling Salesman ◽  
Theoretical Computer ◽  
Annealing Algorithm ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is a problem in combinatorial optimization studied in operations research and theoretical computer science. In this paper, we presented a novel heuristic simulated annealing algorithm for solving TSP. The algorithm is fully operational in the genetic role of crossover operator, and mutation operator, to achieve a balance between speed and accuracy. The experiment results show that the algorithm is better than the traditional method.

The Simulated Annealing Algorithm Based on Multi-Populations Application of TSP

2013 ◽  
Vol 457-458 ◽  
pp. 1037-1041
Author(s):  
Qin Hui Gong
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Traveling Salesman ◽  
Local Minima ◽  
Multiple Populations ◽  
Annealing Algorithm ◽  
Np Problem ◽  
Very High ◽  
Collateral Mechanism

Traveling salesman problem (TSP) is not only a combinatorial optimization problem but also a classical NP problem, which has has high application value. Simulated annealing algorithm is especially effective for solving TSP problems. Based on the deficiency of simulated annealing algorithm on avoiding local minima, this paper has improved the traditional simulated annealing algorithm, proposed simulated annealing algorithm of multiple populations to solve the classical TSP problem. This algorithm has introduced collateral mechanism of multiple populations and increased the initial populations so that it can include more solution set, avoid local minima, thus it has improved the optimization efficiency.This algorithm has very high use value in solving the TSP problem. Keywords: Traveling salesman problem, NP (Non-deterministic Polynomial) problem, simulated annealing algorithm, multiple populations

Modeling and Simulation about TSP Based on Simulated Annealing Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1109-1112 ◽  
Author(s):  
Jian Zhuang Zhi ◽  
Gui Bo Yu ◽  
Shi Jie Deng ◽  
Zhi Ling Chen ◽  
Wen Ya Bai
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Modeling And Simulation ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Traveling Salesman ◽  
Annealing Algorithm ◽  
Simulation Results

The simulated annealing algorithm is applied on traveling salesman problem (TSP), which the genetic algorithm solving in while the earliness phenomena appear. Modeling and Simulation about TSP Based on Simulated Annealing Algorithm have been done. The simulation results have proved that the simulated annealing algorithm is better in searching in the global searching than the genetic algorithm.

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