List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

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 (...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.248-259-016
Main Authors: Zhong, Yiwen, Zhang, Ze-jun, Lin, Juan, Zhan, Shi-hua
Format: Article
Language:English
Subjects:
Algorithms
Cooling
Differential equations, Partial
Intelligence
Lists
Markov analysis
Mathematical research
Neighborhoods
Neural Networks (Computer)
Optimization
Schedules
Simulated annealing
Simulated annealing (Mathematics)
State of the art
Temperature
Travel
Traveling salesman problem
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/1712630