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Mathematical Problems in Engineering
Volume 2017, Article ID 3920327, 11 pages
Research Article

The Improved Ant Colony Optimization Algorithm for MLP considering the Advantage from Relationship

School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan City 430070, China

Correspondence should be addressed to Yabo Luo; moc.361@3791obayoul

Received 27 December 2016; Revised 27 March 2017; Accepted 4 June 2017; Published 31 July 2017

Academic Editor: Gen Q. Xu

Copyright © 2017 Yabo Luo and Yongo P. Waden. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An improved ant colony optimization (ACO) is presented to solve the machine layout problem (MLP), and the concept is categorized as follows: firstly, an ideology on “advantage from quantity” and “advantage from relationship” is proposed and an example is demonstrated. In addition, the strategy of attached variables under local polar coordinate systems is employed to maintain search efficiency, that is, “advantage from relationship”; thus, a mathematical model is formulated under a single rectangular coordinate system in which the relative distance and azimuth between machines are taken as attached design variables. Further, the aforementioned strategies are adopted into the ant colony optimization (ACO) algorithm, thereby employing the inverse feedback mechanism for dissemination of pheromone and the positive feedback mechanism for pheromone concentration. Finally, the effectiveness of the proposed improved ACO is tested through comparative experiments, in which the results have shown both the reliability of convergence and the improvement in optimization degree of solutions.