基于精英蜂群搜索策略的人工蜂群算法

针对人工蜂群(ABC)算法存在收敛速度慢、求解精度不高、容易陷入局部最优等问题，利用蜂群觅食过程中先由侦察蜂进行四处侦察食物，并利用蜂群搜索构建精英群体指导蜂群觅食寻优。据此，提出了一种模拟侦察蜂侦察觅食行为的基于精英蜂群搜索策略的连续优化算法。算法利用构建精英蜂群策略、改进侦察蜂搜索机制以及基于目标函数值选择寻优三个主要策略加强算法的搜索机制。数值实验表明，所提算法不仅寻优精度和寻优率非常高，且收敛速度快，并能适于高维空间的优化问题。

Artificial bee colony algorithm based on elite swarm search strategy

There are some problems in the Artificial Bee Colony (ABC) algorithm, such as the slow convergence speed, low solution precision and easy to fall in local optimum. In this paper, the scout bees firstly explored the food source by a random motivation. Along with the process of colony bee foraging behavior, the elite swarm was constructed to guide the colony bee to achieve better solutions. Hence, the paper proposed a continuous optimization algorithm based on elite swarm search strategy, which simulated the foraging behavior of scout bees. The search mechanism of the algorithm was enhanced by constructing elite swarm strategy, improving the scout bee search mechanism and selecting the best solution based on the objective function value. The numerical experiment results show that the proposed algorithm has high searching precision, success rate and fast convergence speed. It is also suitable for solving high-dimensional space optimization problems.