This paper introduces a new metaheuristic algorithm called bacteria phototaxis optimizer (BPO). It is designed to solving optimization issues. Inspired by the bacteria phototaxis under the control of photosensory proteins in nature, and based on the basic law of bacterial colony growth and evolution, we have designed the photosensory protein concentration, phototaxis motion and growth operators. These three operators exhibit a highly adaptive and information interaction mechanism. The goal is to simulate the phototaxis process of bacteria and form a complete model of BPO. At the same time, BPO is compared with eight most representative as well as newly generated metaheuristics. Its performance is verified by using 23 well-known benchmark functions with three different types. Additionally, we have conducted several evaluation processes, such as qualitative and quantitative analysis as well as parametric and nonparametric tests. Finally, five classical engineering design problems are used to further test the effectiveness of the algorithm in solving constrained problems. The aforementioned experimental results show that compared with other algorithms, BPO has better accuracy, convergence, and robustness and shows strong competitiveness and optimization performance.

本文介绍了一种新的元启发式算法，称为细菌趋光性优化器 (BPO)。它旨在解决优化问题。受自然界中感光蛋白控制下的细菌趋光性的启发，基于细菌菌落生长和进化的基本规律，我们设计了感光蛋白浓度、趋光性运动和生长算子。这三个运营商表现出高度适应性和信息交互机制。目标是模拟细菌的趋光性过程，形成完整的BPO模型。同时，将 BPO 与八个最具代表性的以及新产生的元启发式进行了比较。通过使用具有三种不同类型的 23 个著名基准函数来验证其性能。此外，我们进行了几个评估过程，例如定性和定量分析以及参数和非参数测试。最后采用5个经典工程设计问题进一步检验算法求解约束问题的有效性。上述实验结果表明，与其他算法相比，BPO具有更好的准确性、收敛性和鲁棒性，表现出较强的竞争力和优化性能。