Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

中文翻译：

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基于自适应趋化策略的细菌觅食优化。

细菌觅食优化（BFO）算法是一种新型的群体智能优化算法，已被广泛应用。但是，目前，经典的BFO算法仍然有两个主要缺点：一是固定步长，难以平衡勘探与开发能力。另一个是细菌之间的弱联系，这冒着达到局部最优而不是全局最优的风险。为了克服传统BFO的这两个缺点，本文提出了一种基于自适应趋化性策略（SCBFO）的BFO。在SCBFO算法中，考虑两个方面来设计自适应趋化性策略：基于细菌搜索状态特征的自适应游泳和基于信息交换策略的趋化性翻转的改进。使用CEC 2015基准测试集分析了SCBFO算法的优化结果，并将其与经典和其他改进的BFO算法的结果进行了比较。通过测试和比较，证明SCBFO算法在降低局部收敛风险，平衡勘探与开发，提高算法稳定性方面是有效的。因此，这项研究的主要贡献是SCBFO算法，它提供了一种新颖且实用的策略来处理更复杂的优化任务。使用CEC 2015基准测试集分析了SCBFO算法的优化结果，并将其与经典和其他改进的BFO算法的结果进行了比较。通过测试和比较，证明SCBFO算法在降低局部收敛风险，平衡勘探与开发，提高算法稳定性方面是有效的。因此，这项研究的主要贡献是SCBFO算法，它提供了一种新颖且实用的策略来处理更复杂的优化任务。使用CEC 2015基准测试集分析了SCBFO算法的优化结果，并将其与经典和其他改进的BFO算法的结果进行了比较。通过测试和比较，证明SCBFO算法在降低局部收敛风险，平衡勘探与开发，提高算法稳定性方面是有效的。因此，这项研究的主要贡献是SCBFO算法，它提供了一种新颖且实用的策略来处理更复杂的优化任务。