IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov-Dec;15(6):1865-1876. doi: 10.1109/TCBB.2017.2742946. Epub 2017 Aug 29.
This paper presents a Coevolutionary Structure-Redesigned-Based Bacteria Foraging Optimization (CSRBFO) based on the natural phenomenon that most living creatures tend to cooperate with each other so as to fulfill tasks more effectively. Aiming at lowering computational complexity while maintaining the critical search capability of standard bacterial foraging optimization (BFO), we employ a general loop to replace the nested loop and eliminate the reproduction step of BFO. Hence, the proposed CSRBFO only consists of two main steps: (1) chemotaxis and (2) elimination & dispersal. A coevolutionary strategy by which all bacteria can learn from each other and search for optima cooperatively is incorporated into the chemotactic step to accelerate convergence and facilitate accurate search. In the elimination & dispersal step, the three-stage evolutionary strategy with different learning methods for maintaining diversity is studied. An evaluation of the convergence status is then added to determine whether bacteria should move on to the next stage or not. The combination of coevolutionary strategy and convergence status evaluation is expected to balance exploration and exploitation. Experimental results comparing seven well-known heuristic algorithms on 24 benchmark functions demonstrate that the proposed CSRBFO outperforms the comparison algorithms significantly in most of the cases.
本文提出了一种基于自然现象的协同进化结构重设计细菌觅食优化（CSRBFO）方法，即大多数生物往往相互合作，以更有效地完成任务。针对标准细菌觅食优化（BFO）降低计算复杂度的同时保持关键搜索能力的问题，我们采用一个通用循环代替嵌套循环，并消除 BFO 的繁殖步骤。因此，所提出的 CSRBFO 仅由两个主要步骤组成：（1）趋化作用和（2）消除和扩散。趋化作用步骤中采用协同进化策略，使所有细菌可以相互学习并共同寻找最优解，以加速收敛并促进准确搜索。在消除和扩散步骤中，研究了具有不同学习方法的三阶段进化策略，以保持多样性。然后添加了一个评估收敛状态的步骤，以确定细菌是否继续进入下一个阶段。协同进化策略和收敛状态评估的结合有望平衡探索和开发。在 24 个基准函数上对七个著名启发式算法进行的实验结果表明，所提出的 CSRBFO 在大多数情况下明显优于比较算法。
