【作者】 王辛；

【导师】 邢志栋；

【作者基本信息】 西北大学 ， 计算数学， 2008， 硕士

【摘要】 遗传算法(Genetic Algorithm,简称GA)是一种新兴的演化算法。该算法具有设计简单、容易实现和全局优化能力较强等的优点,因而应用广泛。传统优化算法能够充分利用问题本身所提供的信息与邻域知识,在搜索空间中从一个初始点按照某种确定的原则去寻找下一个迭代点,搜索过程具有针对性,而且收敛速度快、局部寻优能力强。本文设计出一类结合传统优化算法的混合遗传算法。主要工作概述如下:首先,由于基本遗传算法随机性较强,使其存在易产生早熟现象、陷入局部极值点、局部寻优能力差、进化后期收敛慢等缺点。本文正是针对这些问题,提出将遗传算法和传统的优化算法相结合,给出一类混合遗传算法;其次,对算法的收敛性进行了理论分析和数值试验,通过在相关的测试函数(Test function)的数值试验结果中表现出了令人满意的优化性能,说明了算法的有效性;最后又将该类混合遗传算法应用到无约束优化问题和约束问题中。更多还原

【Abstract】 Genetic Algorithm (GA) is a new evolutionary algorithm. The algorithm is simple, easy to implement and has strong global optimization capability, a wider range of applications.Traditional optimization algorithm can take full advantage of the information of the problems provided by the neighborhood knowledge, in accordance with the certain principles to find the next iteration from an initial point in the search space, the search process is targeted, rapid convergence, and has advantages of strong local Optimization.In this paper, I design a kind of hybrid genetic algorithm base on traditional optimization algorithm. Major work summarized as follows:Firstly, because of the strong randomness, standard genetic algorithm easily produce shortcomings,such as premature phenomenon、a local maximum、the poor local optimization and late evolutionary convergence is slow. In light of these problem this paper combine the genetic algorithm and the traditional optimization algorithm,A kind of hybrid genetic algorithms (HGA) based on the traditional optimization is proposed; Secondly, this paper complete the theoretical analysis and numerical experiments for the convergence of the algorithm, Through the relevant test function, the results of numerical experiments demonstrate satisfactory optimal performance and the effectiveness of the algorithm; finally the kind of hybrid genetic algorithm applied to the unconstrained and constrained optimization problem.更多还原