Abstract: In view of the uncertainty of chemotaxis step length and the lack of constancy of elimination diffusion probability in the optimization process of traditional bacterial foraging algorithm, in order to solve the problem of high-dimensional engineering optimization, an emotional bacteria foraging algorithm based on non-uniform elimination-diffusion probability was proposed. Firstly, in the chemotaxis step, the Gus distribution search mechanism was used to update the bacteria individual positions, so as to solve the problem of poor search ability and easy to fall into local optimum caused by bacteria swimming or flipping on each dimension in a random way. The emotion perception factor was introduced, and the sudden change of emotional intelligence was used to realize the adaptive chemotaxis step size, so as to avoid premature convergence of the algorithm. Secondly, in view of the probability constancy of bacterial individuals in the process of elimination-diffusion, the idea of using linear and non-linear probability distributions to replace the traditional constant distribution to realize non-uniform distribution was proposed. By introducing the random value of dynamic factor, the bacterial individuals in the undefined search space were limited, so as to save the calculation cost of the algorithm. Six benchmark functions were used in the test, and the test results show that: in the case of low calculation cost, except on Rosenbrock function, the proposed algorithm has low iteration times and good optimization quality on all functions, and the algorithm convergence comparison results show that the proposed algorithm has good convergence.

Key words: bacterial foraging algorithm, sudden change of emotion, Gus distribution, momentum factor, non-uniform probability distribution

摘要： 针对传统的细菌觅食算法在优化过程中存在的趋化步长的不确定性及消除-扩散概率的恒定性不足的问题，提出一种基于非均匀消除-扩散概率的情绪化细菌觅食算法，以解决高维度工程优化问题。首先，在趋化步骤中利用古斯分布搜索机制对细菌个体位置进行更新，以解决细菌因以随机方式在每个维度上游动或翻转而导致的搜索能力差及易陷入局部最优的问题，引入情绪感知因子，利用情绪智能的突变来实现自适应趋化步长，从而避免算法过早收敛；其次，针对细菌个体在消除-扩散过程中概率的恒定性，提出利用线性和非线性概率分布代替传统的常数分布以此实现非均匀分布的构想，通过引入动力因子随机值，限制未定义的搜索空间中的细菌个体，从而节省算法的计算成本。通过六个基准测试函数进行测试，测试结果表明，在计算成本较低的情况下，除针对Rosenbrock函数外，所提算法针对所有函数均具有较低的迭代次数及良好的优化质量，且算法收敛性对比结果表明所提的算法具有较好的收敛性。

关键词: 细菌觅食算法, 情绪突变, 古斯分布, 动量因子, 非均匀概率分布