Adaptive bee-ant colony optimization
蜂群—蚁群自适应优化算法

This paper proposed a novel colony-ant colony optimization algorithm for continuous function optimization problems.The new algorithm was based on ant colony optimization algorithm.There were two improvements in the new algorithm.Firstly,it devised the adaptive mechanism for parameter q to reduce the parameters’ number and improved the robustness of the new algorithm.Secondly,an efficient local search operator,which used employee bees and observed bees in the artificial bee colony,was devised to enhance the local searching capacity.The simulation results for five benchmark functions show that: compared with those of ant colony optimization,the global and local searching capability of colony-ant colony optimization has been greatly improved.