Let us dive into the first function optimization 讓我們開始優(yōu)化第一個函數(shù)。
Asynchronous parallel evolutionary algorithm for function optimization 函數(shù)優(yōu)化異步并行演化算法
Two asynchronous parallel algorithms for function optimization 求解函數(shù)優(yōu)化問題的兩種異步并行算法
Hybrid genetic algorithm for nonlinear function optimization 一種求解非線性函數(shù)優(yōu)化問題的混合遺傳算法
An improved genetic algorithm for solving function optimization problems 一種用于函數(shù)優(yōu)化的改進(jìn)混合遺傳算法
A generic evolutionary algorithm for solving multi - modal function optimization problems 適用于多峰函數(shù)優(yōu)化問題的通用演化算法
An asynchronous parallel simulated annealing algorithm for function optimization problems 函數(shù)優(yōu)化問題的一種異步并行模擬退火算法
Furthermore , two solution frameworks for objective function optimization problem were given 在此基礎(chǔ)上,針對目標(biāo)優(yōu)化問題,給出兩種求解框架。
The computation results indicate that cga has good computational efficiency in function optimization 通過數(shù)值模擬計算,結(jié)果表明這種引入是有效的。
Kernel function optimization in kernel principle component analysis and its application to feature extraction of gear faults 核主元分析中核函數(shù)參數(shù)選優(yōu)方法研究