人工蜂群算法(artificial bee colony algorithm,ABC)是一种模仿蜜蜂采蜜行为的新兴的群智能优化技术。侦察蜂作为人工蜂群的成员之一,进行随机搜索以找到蜜源。为了弄清楚侦察蜂在ABC中的作用,本文首先分析ABC的生物学机理和主要处理步骤,然后研究当问题维数、种群规模、limit值和最大循环次数等4个控制参数取不同值时对无侦察蜂ABC、单侦察蜂ABC与多侦察蜂ABC性能的影响。实验结果表明,在绝大多数情况下,多侦察蜂ABC求解5个著名的基准函数获得的解优于单侦察蜂ABC和无侦察蜂ABC,而单侦察蜂ABC获得的解优于无侦察蜂ABC。此外,由于这3种算法的搜索复杂度是同阶的,在相同条件下其运行时间相差不大,这充分说明了侦察蜂实施随机勘探过程确实对ABC的性能具有积极意义。Artificial Bee Colony （ABC） algorithm is a new swarm intelligence technique inspired by the foraging behavior of a honeybee swarm. As the member of the artificial bee colony,scout bees carry out random search for discovering food sources. Irt order to investigate the effect of scout bees ort the perfor-mance of ABC,the biological mechanism and main steps of ABC were analyzed,and then,different prob-lem dimensions, population sizes, limit values and maximum cycle numbers were tested on the perfor-mance of ABC under the conditions of no scout bee,single scout bee and multi-scout bees conditions. Al-most all the experimental results show that ABC with multi-scout bees outperforms ABC with single scout bee and ABC without scout bee on five well-known benchmark functions,meanwhile,ABC with single scout bee performs better than ABC without scout bee. Besides,the three algorithms have almost the same execution time under the same conditions due to the same order of their search complexity. These fully demonstrate that the random exploration process adopted by scout bees has positive effect on the performance of ABC.国家自然科学基金资助项目(70971020);广西混杂计算与集成电路设计分析重点实验室开放基金资助项目(2012HCI09);广西民族大学重点科研资助项目(2012MDZD035
