Abstract: A modified Artificial Bee Colony (ABC) algorithm was proposed for numerical function optimization in this paper, in order to solve the problems of slow convergence and low computational precision of conventional ABC algorithm. The modified ABC algorithm can adjust the step size of the selected neighbor food source position adaptively according to the objective function. On the other hand, the searching method based on a nonlinear adjustment of search range depending on the iteration was introduced for scout bees. The modified ABC algorithm can improve the exploitation, and avoids the premature convergence effectively. The experimental results on six benchmark functions show that, the modified ABC algorithm significantly improves the optimization ability. The modified ABC algorithm can achieve the global minimum values for numerous multimodal functions with high dimension. Compared to the other approaches, the proposed method not only obtains higher quality solutions, but also has a faster convergence speed.

Key words: Artificial Bee Colony (ABC), numerical function optimization, neighborhood position, adaptive, nonlinear function

摘要： 针对人工蜂群(ABC)算法存在收敛速度慢、收敛精度低的问题，给出一种改进的人工蜂群算法用于数值函数优化问题。在ABC的邻域搜索公式中利用目标函数自适应调整步长，并根据迭代次数非线性减小侦查蜂的搜索范围。改进ABC算法提高了ABC算法的局部搜索能力，能够有效避免早熟收敛。基于6个标准测试函数的仿真实验表明，改进ABC算法的寻优能力有较大提高，对于多个高维多模态函数该算法可取得理论全局最优解。与对比算法相比，该算法具有更高的收敛精度，并且收敛速度更快。

关键词: 人工蜂群算法, 函数优化, 邻域搜索, 自适应, 非线性函数