进化蚁群算法及其在湖泊富营养化评价中的应用

蚁群算法是近年提出的一种新型的仿生算法,已在许多组合优化问题中得到成功应用,但是传统蚁群算法解决连续优化问题的能力较差.为提高其解决连续优化问题的能力,拓宽应用范围,引入带可变邻域搜索项的进化策略对其进行改进,进而提出进化蚁群算法.随后从2个方面对进化蚁群算法的性能进行测试:①采用多个经典测试函数测试进化蚁群算法用于解决连续优化问题的效果;②将进化蚁群算法应用于千岛湖的富营养化程度评价,以测试该方法解决实际问题的效果.函数测试结果表明,进化蚁群算法可以成功用于解决连续优化问题,并且优化过程所需初始个体的数量少,优化速度快;千岛湖富营养化程度评价实例的结果表明,进化蚁群算法应用于湖泊富营养化评价是可行的,可用于解决实际问题. 

Evolutionary Ant Colony Algorithm and Its Application in Evaluating the Eutrophic State of Lake

Ant colony algorithm(ACA),a kind of new bionics algorithm which has been used in many optimal combination problems successfully,is not good at solving the continuous optimization problems.In order to improve its ability of solving the continuous optimization problems,evolutionary strategy(ES) was taken to modify ACA,and the evolutionary ant colony algorithm(EACA) proposed.After that,two aspects of ways were taken to test EACA.Firstly,several typical functions were used to test whether the EACA can solve the continuous optimization problems;secondly,EACA is applied to evaluate the eutrophic state of Qiandao Lake.The results show that EACA can solve the continuous optimization problem with little initial answer and high speed and can be used to evaluate eutrophic state of lake effectively.