摘要
提出用蚁群算法进行连续参数优化的一种方法 .该方法对解的每一个分量的可能的取值组成一个动态的候选组 ,并对候选组中的每一个值记录其信息量 .在蚁群算法的每一次迭代中 ,首先根据信息量选择解分量的初值 ,然后使用交叉、变异操作来确定解的值 .以非线性规划问题为例所进行的计算结果表明 ,该方法比使用遗传算法具有更好的收敛速度和稳定性 ,克服了蚁群算法不太适合求解连续参数优化问题的缺陷 .
A method for solving optimization problem with continuous parameters using ant colony algorithm is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then the values of the components in the solution can be determined by the operations of cross and mutation. Our experimental results of the problems of nonlinear programming show that our method has much higher convergence speed and stability than that of GA, and the drawback of ant colony algorithm of not being suitable for solving continuous optimization problems is overcome..
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2003年第3期48-53,共6页
Systems Engineering-Theory & Practice
基金
国家自然科学基金 ( 60 0 74 0 1 3 )
国家高性能计算基金 ( 0 0 2 1 9)
江苏省教育厅自然科学基金( 0 2 KJB5 2 0 0 0 9)
南京大学软件新技术国家重点实验室开放基金的资助
关键词
蚁群算法
优化
非线性规划
ant colony algorithm
optimization
nonlinear programming
