论文部分内容阅读
针对细菌觅食算法在优化过程中存在步长一致、速度较慢的缺陷,赋予细菌以灵敏度的概念来调节趋化步长;将分布估计算法的思想引入繁殖算子,对细菌能量较好的半数细菌进行分布估计再生以增加群体的多样性,提高收敛速度;根据细菌的能量情况,赋予细菌自适应迁移概率,对较差的细菌进行随机或指定迁移,以提高算法的全局寻优能力.采用多峰高维标准测试函数对改进算法进行了测试,结果表明,所提出算法有效地提高了搜索速度和精度,改造后可用于多维、约束等实际工程问题的优化.
In the process of bacterial foraging, there exists the same step and slower speed in the optimization process, which gives the bacteria the concept of sensitivity to regulate the chemotaxis step. The idea of distribution estimation algorithm is introduced into the reproductive operator, Half of the bacteria are estimated to be regenerated to increase the population diversity and increase the rate of convergence. According to the energy of the bacteria, the bacteria are given adaptive migration probability, and the poorer bacteria are randomly or specifically migrated to improve the global optimization ability of the algorithm. The multi-peak and high-dimension standard test function is used to test the improved algorithm. The results show that the proposed algorithm can effectively improve the search speed and precision, and can be used to optimize the multi-dimensional and constrained practical engineering problems.