1) inertia scaling fact
惯性加权
1.
Numerical study is carried out using three benchmark functions to the introduced inertia scaling factor MDE algorithm, and the result is compared with that of dynamic differential evolution.
利用三个标准的优化算法测试函数对引入惯性加权系数的微分进化改进算法进行了测试,并与标准微分进化算法进行比较。
2) inertia weight
惯性权重
1.
Research advances on inertia weight in particle swarm optimization;
粒子群优化算法中惯性权重的研究进展
2.
Modified particle swarm optimizer using non-linear inertia weight;
一种非线性改变惯性权重的粒子群算法
3.
Particle swarm optimization with self-adaptive stochastic inertia weight;
具有自适应随机惯性权重的PSO算法
3) inertia weight
惯性权值
1.
Study of particle swarm optimizer algorithm based on dynamic inertia weight;
基于动态改变惯性权值的粒子群算法
2.
Based on the analysis of convergence performance of standard PSO and distribution of solutions of the task assignment problem,the strategy of nonlinearly decreasing inertia weight was adopted in the velocity update formula to improve the convergence of the algorithm,a.
在分析基本微粒群算法的收敛性能和任务分配问题解分布情况的基础上,采用惯性权值非线性下降策略更新微粒速度,以提高算法的收敛性,并且引入一个反正切函数对基本微粒群算法的位置公式进行进一步处理,以保证解的可行性。
3.
In order to eliminate the shortcomings of traditional neural networks in handwritten Chinese characters recog-nition, such as the premature convergence, a novel intelligent method is presented, which uses the particle swarm opti-mization (PSO) algorithm with adaptive inertia weight to train the neural networks.
为了克服手写体汉字识别中传统神经网络训练算法存在网络易于过早收敛的缺陷,本文提出采用自适应惯性权值的粒子群优化算法训练神经网络,即利用粒子更新迭代训练神经网络最优的权值和阈值,其中对粒子更新的惯性权值进行了自适应性的改进。
4) inertia weight
惯性权
1.
The effects of inertia weight on particle swarm optimization(PSO) performance are analyzed.
分析惯性权值对粒子群优化算法(PSO)优化性能的影响,提出了基于自适应随机惯性权(ARIW)的改进粒子群优化算法。
2.
A new particle swarm algorithm with dynamically changing inertia weight(DCW) is presented to solve the problem that the linearly decreasing weight(LDW) of the particle swarm algorithm cannot adapt to the complex and nonlinear optimization process.
针对惯性权值线性递减粒子群算法(LDW)不能适应复杂的非线性优化搜索过程的问题,提出了一种动态改变惯性权的自适应粒子群算法(DCW)。
3.
The effects of inertia weight on particle swarm optimization (PSO) performance are analyzed.
分析了惯性权值对粒子群优化 (PSO)算法优化性能的影响 ,进而提出选择惯性权值的新策略 。
5) Inertia weight factor
惯性权因子
6) random inertia weight
随机惯性权
1.
Aiming at the deficiency of the traditional particle swarm optimization,the new zoom model calculated by particle swarm optimization based on adaptive random inertia weight is put forward.
针对基本粒子群算法在处理高维复杂问题时易陷入局部极值点的不足,提出自适应随机惯性权的粒子群优化算法,并基于此算法建立起洪水过程的放大模型。
补充资料：惯性
惯性
inertia
惯性(inertia) 惯性是物质的一种性质，表现为物体对一其变化的阻抗。因此，当没有外力作用时，原来处止状态的物体将保持静止、而原来在运动中的将以不变的速度继续作直线运动(牛顿第一运律)。物体的质量是它的惯性的度量。参阅“寿(mass)条。 仁内代尔斯基(1.Nedelsky)动静淋淀剔阳
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