动态高斯变异和随机变异融合的自适应细菌觅食优化算法

Adaptive Bacterial Foraging Optimization Algorithm Based on Dynamic Gaussian Mutation and Random One for High Dimensional Functions

针对细菌觅食优化(Bacterial Foraging Optimization,BFO)算法在高维函数优化上性能较差和普适性不强的问题,提出一种动态高斯变异和随机变异融合的自适应细菌觅食优化算法。首先,将原随机迁徙方案修改为动态高斯变异与随机变异融合的迁徙方法,即搜索前期利用随机迁徙有利于增加解的多样性,获得全局最优解,搜索后期改用动态的高斯变异来提高算法的收敛速度;然后,对趋化操作中的步长参数使用动态调整和自适应调整来增强算法的普适性;最后,构建全局极值感应机制使优化更有效,从而获得了一种高性能的自适应BFO算法,以便能够高效解决高维函数的优化问题。14个高维函数优化的仿真结果表明,提出的算法不仅优化效果好、普适性强,而且能以更快的速度找到全局最优解,性能优于SBFO、POLBBO、BFAVP和RABC算法。

In view of the shortcomings of facterial foraging optimization （BFO）, such as the bad optimization perfor- mance and generalization in its application of high dimensional function optimization, an adaptive bacterial foraging opti- mization algorithm based on combing dynamic Gaussian mutation and random one was proposed in this paper. First, the original elimination-dispersal operator is replaced with a new one based on combining random mutation to add popula- tion diversity and dynamical Gaussian mutation to raise convergence rate. Then a chemotactic step mechanism is adopted with dynamical adjusting and self-adapting adjusting. Finally, a new communication mechanism is added to the improved BFO. The simulation results on 14 high-dimensional functions indicate that the proposed optimization algorithm is rapid and has good performance and generalization, and outperforms the current global optimization algorithms such as SBFO, POLBBO, BFAVP and RABC.