Abstract: The standard artificial bee colony algorithm is limited in solving minimum function optimization problems because of the poor local search ability, low computational precision of convengence and easily to fall to local optimum. Attempting to solve the problems emphasized above, this paper proposes an advanced artificial bee colony algorithm. This algorithm introduces chaos operator into the local search strategy of employed bees and onlooker bees, which is based on the current optimal solution. Also, this algorithm introduces bacterial chemotaxis behavior into onlooker bees to improve its local search ability. The simulation results of six test functions show that the algorithm can effectively avoid getting into local optimum, and the convergence accuracy is significantly improved.

Key words: artificial bee colony, current optimal, chaos operator, bacterial chemotaxis behavior

摘要： 标准人工蜂群算法由于局部搜索能力差，收敛精度低，容易陷入早熟收敛等缺陷，从而求解最小值函数优化问题的能力受到限制。为了解决标准人工蜂群算法的以上问题，提出了一种改进的人工蜂群算法。该算法将混沌算子引入雇佣蜂和跟随蜂基于当前最优解的局部搜索策略中，并赋予跟随蜂细菌的趋药性，从而提高了人工蜂群算法的局部搜索能力。在6个测试函数上的仿真结果表明，该算法能有效地避免陷入局部最优，并使收敛精度得到显著提高。

关键词: 人工蜂群算法, 当前最优解, 混沌算子, 细菌趋药性