Adaptive bacterial colony chemotaxis multi-objective optimisation algorithm
by Guo-yan Meng; Yu-lan Hu; Yun Tian; Qing-Shan Zhao
International Journal of Computing Science and Mathematics (IJCSM), Vol. 5, No. 4, 2014

Abstract: This paper focuses on the multi-objective optimisation problem (MOOP). To improve the convergence speed and the diversity of bacterial chemotaxis multi-objective optimisation algorithm (BCMOA) and avoid falling into local minimum, this paper proposes an adaptive bacterial colony chemotaxis multi-objective optimisation (ABCCMO) algorithm. Firstly, fast non-dominated sorting approach is used to initialise the position of all the bacterial. Secondly, this proposed algorithm adopts the adaptive chemotaxis step length. Thirdly, colony intelligent optimisation thought is adopted. Experimental results show that ABCCMO is able to find much better Pareto front solutions.

Online publication date: Sat, 31-Jan-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