改进人工蜂群算法在饲料配比中的应用研究

人工蜂群算法是一种模拟蜜蜂采蜜的群智能优化算法。针对传统的人工蜂群算法收敛速度慢,容易陷于局部最优进行了改进,引入了扰动控制频率来指导引领峰搜寻蜜源,增强算法局部搜索能力。提出了自适应动态变异算子,提高了算法收敛速度。融合了Boltzmann策略选择机制,动态调整了算法的搜索范围,增强了种群的多样性。算法成功地应用到求解动物饲料配比问题。结果显示,在运行效率、最优解质量、稳定性均优于被比较的其它算法。

Improve Artificial Bee Colony Algorithm and its application of feed ratio

Artificial colony algorithm is a kind of simulation in the group of honey bees group of intelligent optimization algorithms. the traditional artificial colony algorithm has slow convergence speed, and Limits to local optimum .An improved algorithm is proposed, which introduces disturbance control frequency to guide employed bees searching for source, enhance algorithm local searching ability; introducing the adaptive dynamic mutation operator factor to improve the algorithm convergence speed; the strategy of choice for Boltzmann is adopted with dynamic adjustment of the search area of the algorithm, and enhancement the diversity of population, the algorithms of this paper are successfully applied to practical animal feed in proportion problems, the experimental results show that above algorithm is better than other compared algorithms in efficiency, the optimal solution quality and stability.