基于群体信息挖掘的新遗传算法及其应用

The genetic algorithm based on colony information mining and its application

鉴于标准遗传算法有局部搜索能力差,求解精度不高等缺点,引入多元回归分析和模式搜索算法,利用最小二乘法求得种群寻优方向的信息,建立一种新的遗传算法,并由此指导种群中的每一个个体模式搜索,提高整个种群的寻优速度和效能,以非线性连续优化仿真为例,结果表明,该方法比遗传算法性能更好。该算法应用于优化化工过程,效果良好。

The multiple regression analysis and pattern search algorithm were joined into the standard genetic algorithm to solve the defect of poor local search ability and bad precision of optimal result, using the information of search direction gained by the least square to constitute a new genetic algorithm, the optimization rate and efficiency of the entire colony was improved under the each individual of entire colony doing pattern search with that information, the simulation result of non-linear continuous optimization indicates that the algorithm has better performance than genetic algorithm, the effect of simulation is satisfaction when the algorithm was used to optimize chemical progress.