An improved bacterial colony chemotaxis multi-objective optimisation algorithm
by Qing-shan Zhao; Yu-lan Hu; Yun Tian
International Journal of Computing Science and Mathematics (IJCSM), Vol. 4, No. 4, 2013

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.

Online publication date: Sat, 10-May-2014

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