摘要
混沌优化算法和遗传算法的结合产生了变尺度混沌遗传算法(MSCGA)。该算法在不改变GA搜索机制的同时,根据搜索进程,不断缩小优化变量的搜索空间及调节系数,引导种群进行新一轮进化,从而产生更优的最优个体,改善了GA的性能。但是通过分析其本质,发现其中存在很大的重复性操作,没有考虑它们之间在优化过程中的某种相似之处。文章中对此算法进行讨论并对其进行优化。计算机仿真表明:优化后的算法具有更好的快速寻优能力。
Mutative scale chaos genetic algorithm（MSCGA） combination of chaos optimization method and genetic algorithms.The character of this new method is that the mechanism of the GA is not changed but the search space and the coefficient of the adjustment of the optimization parameter are reduced continually,which leads to generation evolution to the next generation in order to pro-duce better optimization individuals so as to improve the performance of the GA.But by analyzing it essence,many repetitions are found,without considering the certain resemblance between them in the optimization process.And some improvements are made there.Through computer simulating,it indicates that the Improved chaos genetic algorithms en joy a better searching ability.
出处
《计算机与数字工程》
2010年第4期1-3,10,共4页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:60173055)资助
关键词
几何约束求解
混沌优化算法
变尺度混沌遗传算法
优化
geometric constraint solving
chaos optimization method
mutative scale chaos genetic algorithm
optimization
