Title: An improved bacterial colony chemotaxis multi-objective optimisation algorithm

Authors: Qing-shan Zhao; Yu-lan Hu; Yun Tian

Addresses: Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China ' Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China ' Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China

Abstract: This paper focuses on the multi-objective optimisation problem (MOOP). To improve the convergence speed and diversity of bacterial chemotaxis multi-objective optimisation algorithm (BCMOA) and overcome the defects of escape from local minimum, this paper proposes an improved bacterial colony chemotaxis multi-objective optimisation algorithm (IBCCMOA). Firstly, fast non-dominated sorting approach is used to initialise the position of all the bacterias. Secondly, colony intelligent optimisation thought is adopted. Thirdly, a strategy of elite reserve is applied to avoid abandoning the points that the original position is good. Experimental results show that the convergence and diversity solutions of the proposed algorithm are better than that of the existing BCMOA.

Keywords: multi-objective optimisation; MOO; fast non-dominated sorting; bacterial colony chemotaxis optimisation; intelligent optimisation; elite reserve.

DOI: 10.1504/IJCSM.2013.058063

International Journal of Computing Science and Mathematics, 2013 Vol.4 No.4, pp.392 - 401

Received: 29 May 2013
Accepted: 01 Jul 2013

Published online: 01 Dec 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article