Surface ship target recognition research based on sga 應用基本遺傳算法進行水面艦船目標識別研究
This paper introduces the basic ga and its development , and provide an arithmetic to the diagnosis equations with ga 首先討論了基本遺傳算法及其改進算法,結合故障診斷方程的特點,提出了基于ga算法的故障診斷方程求解方法。
Chaos optimization search operation is introduced to simple genetic algorithm operation for mending the defect that simple genetic algorithm is premature easily 在基本遺傳操作中引入了混沌優(yōu)化搜索操作,克服了基本遺傳算法容易“早熟”的缺陷。
Compared with the computational result of traditional ga , it shows that the searching efficiency of ga can be improved remarkably and the fluctuation of random searching can be reduced by recognizing building block 與基本遺傳算法的計算結果對比分析表明,所提算法可顯著提高遺傳算法的搜索效率,減小遺傳算法隨機搜索的波動性。
In this thesis , following improving of the simple genetic algorithm , the improved genetic algorithm is used to solve the problem of logistics distribution center location , getting the resolution of the location model 本文在改進基本遺傳算法基礎上,然后利用該改進的遺傳算法對物流配送中心選址問題進行優(yōu)化求解,并結合實際模型,提出了“混合并行編碼”的編碼思想。