MATEC Web Conf.
Volume 128, 20172017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
|Number of page(s)||5|
|Section||Simulation Model and Algorithm|
|Published online||25 October 2017|
Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems
School of Physics and Electronic Information, HuaiBei Normal University, Huaibei, 235000, China
a Corresponding author: Feng Zou email@example.com
In this paper, a discrete variant of TLBO (DTLBO) is proposed for solving the traveling salesman problem (TSP). In the proposed method, an effective learner representation scheme is redefined based on the characteristics of TSP problem. Moreover, all learners are randomly divided into several sub-swarms with equal amounts of learners so as to increase the diversity of population and reduce the probability of being trapped in local optimum. In each sub-swarm, the new positions of learners in the teaching phase and the learning phase are generated by the crossover operation, the legality detection and mutation operation, and then the offspring learners are determined based on greedy selection. Finally, to verify the performance of the proposed algorithm, benchmark TSP problems are examined and the results indicate that DTLBO is effective compared with other algorithms used for TSP problems.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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