人工蜂群演算法為近年來熱門的最佳化演算法；但此演算法的缺點為易於陷入區域最佳解。本研究針對人工蜂群演算法的缺點改良，提出運用最佳訊息與活化策略的改良式人工蜂群演算法；運用最佳訊息的移動策略加快收斂速度，活化策略用於陷入區域最佳解的跳脫方式。結果顯示，本研究提出新的演算法，不論應用於單峰或多峰等多種測試函數，求解能力優於標準人工蜂群演算法及相關研究。

Artificial Bee Colony algorithm (ABC) is one of the most popular algorithms for solving optimization problems in recent years. But it still has the problem of falling into local optimal solution space.In this paper, we propose a novel artificial bee colony algorithm using the global best information and activation strategy to improve the drawbacks of original ABC algorithm. Utilizing the global best solution can speed up the convergence, and the activation strategy is able to provide better exploration capability. The experiment results show that our algorithm is better than the original ABC and related researches one in the uni-modal and multi-modal benchmark functions.