1) genetic-simulated-annealing
遗传退火
1.
Knowledge based genetic-simulated-annealing method for optimizing tube circuit of fin-and-tube heat exchangers;
基于知识及遗传退火混合算法的翅片管换热器管路优化方法
2) annealing genetic
退火遗传
1.
By combining the artificial scheme with the algorithmic scheme, a new annealing genetic algorithm based on humanmachine interaction were put forward, being aimed at global optimal problems with complicated characteristics of largescale, nonlinear, nonconvex in the engineering.
针对工程中存在的许多具有大规模、非线性、非凸等复杂特性的全局优化问题,在退火遗传算法的基础上,通过将人工方案和算法方案相结合,提出了一种基于人机交互的退火遗传算法。
3) annealing genetic algorithm
退火遗传算法
1.
Fuzzy-PID temperature control system based on annealing genetic algorithm;
基于退火遗传算法的Fuzzy-PID温控系统
2.
Short-term optimal operation of hydropower station based on annealing genetic algorithm;
基于退火遗传算法的水电站短期优化调度
3.
In order to get the optimal identification of the optical properties parameters,annealing genetic algorithm according to the superficial diffuse reflection photon distribution,which emerged when a collimated laser beam normally incident upon and propagated through the tissue,is proposes.
为求取最优化的光学特性参数,基于准直激光束通过均匀生物组织时所形成的表面漫反射光分布,提出利用退火遗传算法重构生物组织的光学特性参数,用此方法成功地实现了对强散射均匀生物组织多个光学特性参数的联合重构。
4) genetic annealing algorithm
遗传退火算法
1.
Decorrelating multi-user detection based on Genetic Annealing Algorithm;
基于遗传退火算法的解相关多用户检测器
2.
Protein Folding Structure Prediction Based on Genetic Annealing Algorithm in AB Off-Lattice Model;
基于AB非格模型与遗传退火算法的蛋白质折叠结构预测
3.
The genetic algorithm and genetic annealing algorithm are used to optimize the objective function for realizing the optimization design to.
为进一步解决交通拥堵,以城市道路多个单点信号控制交叉口组成的绿波系统为研究对象,建立一个以干线车辆行程时间最短为目标,各相位有效绿灯时间、饱和度及周期时长为约束条件的非线性函数模型,分别运用遗传算法和遗传退火算法对目标函数进行优化,实现了对绿波系统中各交叉口信号配时的优化设计,并以实例加以论证,结果表明:遗传退火算法更能快速、准确地寻找出全局最优解。
5) Genetic Simulated Annealing
遗传模拟退火
1.
The application of a Genetic Simulated Annealing(GSA)algorithm as an optimization method for finding a suitable combination.
提出了微观仿真模型参数校正流程,然后以合肥市大东门区域VISSIM仿真系统模型为实例,建立了仿真模型参数校正的遗传模拟退火启发式算法,实现了对VISSIM的仿真参数的自动化校正,根据实测结果和仿真实验比较分析,验证了算法的有效性。
2.
Firstly,the quasi-maximum likelihood estimation,combining genetic simulated annealing (GSA) and maximum likelihood estimation (MLE),is developed for the parameter estimation of the model.
因此首先利用遗传模拟退火算法估计门限参数和同积向量,然后用极大似然估计计算其余的参数,仿真结果表明,拟极大似然估计不受模型维数限制具有有效性和可行性,此外,数值计算结果的比较分析表明遗传模拟退火优于传统的遗传算法、模拟退火和随机搜索等优化算法。
6) algorithms of GA-Annealing strategy
遗传退火算法
1.
A general stochastic neural network(GFSNN),which membership functions are general Gaussian functions and are adaptable,is proposed to predict chaotic time series,and the model s structure and parameters are optimized by the algorithms of GA-Annealing strategy and are applied to forecast stochastic chaotic time series.
针对随机模糊神经网络缺乏自适应性,引入广义高斯函数和广义随机模糊神经网络,使系统中隶属函数具有自适应性;并对参数进行遗传退火算法优化,使系统具有最佳结构和参数。
2.
Chaotic system identification based on adaptable T-S fuzzy model and algorithms of GA-annealing strategy;
针对混沌系统辨识引入广义T-S模糊模型,使系统中隶属函数具有自适应性;并对T-S模糊模型前件模糊规则数、各加权值、隶属函数自适应参数进行遗传退火算法优化,使系统具有最佳结构和参数。
补充资料：软化退火(见低温退火)
软化退火(见低温退火)
soft-annealing
rU0nhUO tUihUO软化退火(soft一annealing)见低温退火。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条