scholarly journals List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shi-hua Zhan ◽  
Juan Lin ◽  
Ze-jun Zhang ◽  
Yi-wen Zhong
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Solution Space ◽  
Traveling Salesman ◽  
Maximum Temperature ◽  
Acceptance Criterion ◽  
Wide Range ◽  
Key Factor ◽  
Cooling Schedule

Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters’ setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.

MEMBRANE ALGORITHM WITH BROWNIAN SUBALGORITHM AND GENETIC SUBALGORITHM

2007 ◽  
Vol 18 (06) ◽  
pp. 1353-1360 ◽  
Author(s):  
TAISHIN Y. NISHIDA
Keyword(s):  
Simulated Annealing ◽  
Approximate Solutions ◽  
Search Space ◽  
Traveling Salesman ◽  
Benchmark Problems ◽  
Wide Range ◽  
Membrane Algorithm ◽  
Thermal Movement ◽  
The Traveling Salesman Problem ◽  
Cooling Schedule

Membrane algorithms with subalgorithms inspired by simulated annealing are treated in this paper. Simulated annealing is inherently a kind of local search but it modifies a solution to a worse one with a probability determined by "temperature". The temperature of simulated annealing is changed according to "cooling schedule". On the other hand, the subalgorithm introduced here has constant temperature which is determined by the region where the subalgorithm is. It is called Brownian subalgorithm since the subalgorithm incorporates "thermal movement" of a solution in the search space but does not simulate "annealing". Computer simulations show that a membrane algorithm which has three regions and has a Brownian subalgorithm in each region can obtain very good approximate solutions for several benchmark problems of the traveling salesman problem. However, the algorithm, occasionally, gets quite bad solutions (twice as large as the optimum) for a problem. A membrane algorithm which has both Brownian and genetic subalgorithms never gets such a bad solution (only 8% worse than the optimum) for all problems examined, although, in average, it is not as good as the algorithm with Brownian only. The result indicates that membrane algorithm with subalgorithms under different approximate mechanisms may be robust under a wide range of problems.

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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