Adaptive bacterial colony chemotaxis multi-objective optimisation algorithm
Guo Yan Meng, Yu Lan Hu, Yun Tian, Qing Shan Zhao · International Journal of Computing Science and Mathematics · 2014
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.