具有自适应趋向性和引导因子的人工蜂群算法

针对人工蜂群算法中存在的收敛速度慢、寻优精度低的问题,提出了一种改进的人工蜂群算法。该算法将自适应趋向性加入雇佣蜂的搜索方案中,同时在观察蜂的搜索方案中加入引导因子。通过雇佣蜂对优秀蜜源的动态趋向搜索以及观察蜂在引导因子引领下的协同搜索,显著提高了算法的局部搜索能力。基于八个标准测试函数的仿真结果表明,与基本人工蜂群算法相比,改进后的算法在寻优精度和收敛速度方面均有明显提升。

An artificial bee colony algorithm with adaptive chemotaxis and guiding factors

We propose an improved artificial bee colony (ABC) algorithm to overcome the drawbacks of slow convergence rate and low optimization precision of the conventional ABC algorithm. The self adaptive chemotaxis and guiding factors are introduced into the search schemes of employed bees and onlooker bees, respectively. Under the guidance of the guiding factors, the local search capability is significantly improved due to the employed bees’ dynamic search for the high quality nectar sources and the onlooker bees’ cooperative search for the better nectar sources. Simulation results based on the eight standard functions show that the improved ABC algorithm is superior to the conventional one in both computational accuracy and convergence rate.