Title: Adaptive bacterial colony chemotaxis multi-objective optimisation algorithm

Authors: Guo-yan Meng; Yu-lan Hu; Yun Tian; Qing-Shan Zhao

Addresses: Department of Mathematics, 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 ' 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 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.

Keywords: multi-objective optimisation; MOO; adaptive chemotaxis step length; bacterial chemotaxis; adaptive bacterial colony optimisation; convergence speed; diversity.

DOI: 10.1504/IJCSM.2014.066449

International Journal of Computing Science and Mathematics, 2014 Vol.5 No.4, pp.336 - 345

Received: 30 Jun 2014
Accepted: 30 Aug 2014

Published online: 31 Jan 2015 *

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