【作者】 曹春红； 李文辉； 张永坚；

【Author】 Cao Chunhong~1; Li Wenhui~1;Zhang Yongjian~2 1(College of Computer Science and Technology, Jilin University, Changchun 130012, China)2(Dept, of Information and Electronical Engineering,Shandong Institute of Architecture and Engineering, Jinan 250014, China)

【机构】 吉林大学计算机科学与技术学院； 山东建筑工程学院信息与电气工程系；

【摘要】 约束问题可以转化为优化问题。引入GAAA(遗传蚂蚁算法)解决几何约束问题。为了充分利用遗传算法的快速性,随机性和全局收敛性,在算法的前期采用遗传算法。它的结果是产生信息素的初始分布。算法的后期采用蚂蚁算法。因为有初始信息素的分布,具有并行、有反馈性和高效的求解效率。由于在遗传算法中使用随机的种群,这样不仅能够提高蚂蚁算法的速度,而且在求精解的时候能够避免陷入局部最优解。算法具有良好的优化性能和时间性能。更多还原

【Abstract】 The constraint problem can be transformed to an optimization problem. It introduced GAAA (genetic algorithm-ant algorithm) in solving geometric constraint problems, and adopted genetic algorithm in the former process of algorithm so that it can make use of the fastness, randomicity and global stringency of genetic algorithm. The result is to produce the initiatory distribution of information elements. In the latter process of the algorithm the ant algorithm was adopted. In the condition that there are some initiatory information elements, it can utilize fully the parallel, feedback and the high solving efficiency. Using random colony in the genetic algorithm, this can not only improve the speed of ant algorithm but also avoid getting in the local best solution when solving the precise solutions. The algorithm has a good effect in not only optimization capability but also time capability.更多还原