Paper Info
Title
Hybrid simulated annealing algorithm based on adaptive cooling schedule for TSP
Abstract
The traveling salesman problem is one of the most notoriously intractable NP-complete optimization problems. Over the last 10 years, simulated annealing and tabu search have emerged as an effective algorithm for the TSP. However, the quality of solutions found by using tabu search approach depends on the initial solution and the iteration process of simulated annealing is slow. To overcome this problem and provide an efficient methodology for the TSP, the heuristic search approach based on simulated annealing which combining tabu search strategy and two neighborhood perturbation factor is developed. The proposed hybrid algorithm is tested on standard benchmark sets and compared with the conventional simulated annealing algorithm. The computational results show that the proposed algorithm has significantly better convergence speed compared with conventional simulated annealing algorithm and can obtain high-quality solutions within reasonable computing times.
Year
DOI
Venue
2009
10.1145/1543834.1543969
GEC Summit
Keywords
Field
DocType
tabu search,intractable np-complete optimization problem,proposed hybrid algorithm,simulated annealing,tabu search strategy,conventional simulated annealing algorithm,hybrid simulated annealing algorithm,tabu search approach,heuristic search approach,proposed algorithm,adaptive cooling schedule,effective algorithm,adaptive,optimization problem,simulated annealing algorithm,hybrid algorithm,adaptive simulated annealing,traveling salesman problem,heuristic search
Simulated annealing,Hill climbing,Mathematical optimization,Hybrid algorithm,Computer science,Adaptive simulated annealing,Travelling salesman problem,Artificial intelligence,Optimization problem,Tabu search,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
4
0.56
6
Authors
3
Name
Order
Citations
PageRank
Yi Liu1265.49
Shengwu Xiong218953.59
Hongbing Liu3598.74