Solving travelling salesman problem using multiagent simulated annealing algorithm with instance-based sampling
by ChangYing Wang; Min Lin; YiWen Zhong; Hui Zhang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 6, No. 4, 2015

Abstract: Simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitations, we present a multi-agent SA algorithm with instance-based sampling (MSA-IBS) by exploiting learning ability of instance-based search algorithm to solve travelling salesman problem (TSP). In MSA-IBS, a population of agents run SA algorithm collaboratively. Agents generate candidate solutions with the solution components of instances in current population. MSA-IBS achieves significant better intensification ability by taking advantage of learning ability from population-based algorithm, while the probabilistic accepting criterion of SA keeps MSA-IBS from premature stagnation effectively. By analysing the effect of initial and end temperature on finite-time behaviours of MSA-IBS, we test the performance of MSA-IBS on benchmark TSP problems, and the algorithm shows good trade-off between solution accuracy and CPU time.

Online publication date: Fri, 04-Sep-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com