引入跟踪搜索和免疫选择的人工蜂群算法

针对人工蜂群算法中食物源更新和观察蜂选择食物源机制存在的缺点，提出一种具有跟踪搜索和免疫选择的人工蜂群算法。在原搜索方法基础上，引入跟踪全局最优解和随机选择解的搜索方法，选择搜索到的最优解作为候选解，以加快种群的收敛速度，提高算法的收敛性;在观察蜂选择食物源时，引入免疫系统的抗体浓度调节机制，以维持种群的多样性，提高算法的全局搜索能力。对6个经典测试函数的仿真计算结果表明，与ABC、GABC、RABC和TABC算法相比，改进算法在寻优精度、收敛性能方面具有较明显的优势。

Artificial Bee Colony Algorithm with Tracking Search and Immune Selection

To overcome the disadvantages of food source updating and the mechanism for onlooker bees to select food source in the Artificial Bee Colony (ABC) algorithm,an ABC algorithm based on tracking search and immune selection is proposed. The search methods for tracking the global optimal solution and randomly selecting solution are introduced on the basis of the original solution searching method. The searched optimal solution is selected as the candidate in order to accelerate the convergence of the population and improve the convergence of the algorithm. For the procedure of the onlooker bees selecting the food source,the regulation mechanism of antibody density in the immune system is introduced to keep the diversity of the population and enhance the global search ability of the traditional algorithm. The simulation results for 6 classical benchmark functions show that the improved algorithm has obvious advantages in the optimization accuracy and convergence rate compared with the original ABC,GABC,RABC and TABC.