一种改进蚁群算法及其在配方优化中的应用  

Improved Ant Colony Algorithm and Its Application to the Optimization of Recipe

作　　者：郑松[1] 侯迪波[1] 唐旭华[2] 叶波[1] 周泽魁[1] 

机构地区：[1]浙江大学工业控制技术国家重点实验室,浙江杭州310027 [2]化学工业出版社,北京100029

出　　处：《江南大学学报（自然科学版）》2008年第4期387-391,共5页Joural of Jiangnan University (Natural Science Edition)　

基　　金：国家自然科学基金重点项目(NSFC-50505045)

摘　　要：针对蚁群算法在解决组合优化问题时存在演化过程收敛慢、耗时长的缺点,提出了将确定性搜索移动引入蚁群算法中,并研究了改进后蚁群算法在啤酒配方优化中的应用.在满足生产指标前提下,实现配方的原料总成本最低.应用结果表明:针对啤酒配方优化问题,改进的蚁群算法,具有更强的全局搜索能力和鲁棒性,并易于实现,具有较好的应用价值.Aiming at the disadvantages of slow convergence and time-consuming in the process of evolution when ant colony algorithm（ACA） slove the eombinatoral optimization, the determinately searching motion is added in the ACA, and the study of new ACA of the formulation of beer recipe is presented in the paper, which in meeting production targets premise, achieve the lowest total cost of the raw materials. The results show that compared with the traditional ACA, the improved ACA has more global search capability and robustness, and ease of implementation, has practical value.

关 键 词：优化 蚁群算法 全局搜索 啤酒配方 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]