摘要
针对常用优化算法求解时实时性较差且易陷于局部最优解的问题,提出一种新型快速的直接随机优化算法(DROA).该算法直接利用随机搜索过程寻找最优解,减少了额外计算,降低了计算复杂度;其搜索过程分为全局搜索和局部搜索两个阶段,各阶段选用不同的调节参数公式和搜索方式.先将递增参数的3个随机优化模块串接构造全局优化子,并将多个全局优化子并行搜索构造全局优化器以获得全局最优解;再将多个局部优化模块串接在一起运行构造局部优化器使优化解更精确.测试结果表明,该方法快速高效,优于目前的全局优化算法.
As the current stochastic optimization algorithms almost simulate evolutional process to solve the real optimization problems and the searching result can＇ t reach the optimum solution, and it is difficult to use them in the real-time application, a novel, fast and direct random optimization algorithm was proposed. The random search was directly used to find the optimum to cut off the additional time, and the random search process was divided into two different phases. In the first one a global optimizer was created through connecting three sub-optimizers including increasing parameters in serials and a global optimization module was formed with the paralleling optimizers to get a global solution; in the second one local optimization module was created to obtain more precisean optimum. The tests for quite a few complicated functions indicate that the proposed optimization algorithm is rapid and effective and outperforms the current global optimization algorithms.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2012年第4期750-756,共7页
Journal of Jilin University:Science Edition
基金
河南省重点科技攻关项目(批准号:092102210017)
关键词
优化法
直接随机优化算法(DROA)
全局搜索
局部搜索
函数优化
optimization method
direct random optimization algorithm （DROA）
global search
localsearch
function optimization
