本文主要介紹了一種新型的、隨機(jī)性的全局優(yōu)化方法即遺傳算法。一般應(yīng)用于在一個(gè)
問(wèn)題的解集中查找最優(yōu)解情況,如是一個(gè)問(wèn)題有多個(gè)答案,但是想查找一個(gè)最優(yōu)答案的話,那么使用遺傳算法可以達(dá)到更快更好的效果。即在浮點(diǎn)編碼遺傳算法中加入一個(gè)函數(shù),構(gòu)成適于不可微函數(shù)全局優(yōu)化的遺傳算法。該算法改善了遺傳算法的局部搜索能力,顯著提高了遺傳算法求得全局解的概率。
關(guān)鍵詞: 遺傳算法; 函數(shù)極值; 全局最優(yōu)
Abstract: A hybrid computational intellective algorithm for locating the global optima of in
differentiable nonlinear function was put forward by setting the Powell algorithm in real-code
genetic algorithm. The hybrid approach improved the local searching ability of the genetic
algorithm and promoted the probability for the global optima greatly. Because only the objective values are used, the hybrid approach is a generalized genetic algorithm for global optima of differentiable and indifferentiable nonlinear functions.
Keywords: genetic algorithms; the function pole value; global optima