MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||5|
|Section||Electronics, Information and Engineering Application|
|Published online||08 March 2016|
Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem
Materiel Management & Safety Engineering College, Air Force Engineering University, Xi’an 710043, China
2 Information Management Centre, Air Force Engineering University, Xi’an 710043, China
3 Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an 710043, China
1 Corresponding author: email@example.com
The double evolutional artificial bee colony algorithm (DEABC) is proposed for solving the single depot multiple traveling salesman problem (MTSP). The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-of-the-art methods.
Key words: double evolutional artificial bee colony algorithm / optimization / multiple travelling salesman problem
© Owned by the authors, published by EDP Sciences, 2016
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.