Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems

0Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Wu, L., Zoua, F., & Chen, D. (2017). Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems. In MATEC Web of Conferences (Vol. 128). EDP Sciences. https://doi.org/10.1051/matecconf/201712802022

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free