The improved particle swarm optimization algorithm is applied to path planning problem in 3-D space.
将改进粒子群优化算法应用于三维空间路径规划。
An improved particle swarm optimization algorithm embedded with greedy search for solution of unit commitment.
求解机组组合问题的嵌入贪婪搜索机制的改进粒子群优化算法。
This paper presents an improved particle swarm optimization algorithm based on P2P streaming media data scheduling method.
本文提出一种基于改进粒子群算法的P 2 P流媒体数据调度方法。
This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and puts forward an improved particle swarm optimization algorithm.
介绍基本粒子群优化算法的原理、特点，并在此基础上提出了一种改进的粒子群算法。
In this paper, the improved particle swarm optimization algorithm for the single level capacitated dynamic lot-sizing problem is presented. The detailed realization of the algorithm is illustrated.
本文提出了用于求解单级多资源约束的生产批量计划问题的改进二进制粒子群算法，阐明了算法的具体实现过程。
This paper brings forward the binary improved particle swarm optimization algorithm for decision of loans combinatorial optimization problem, and illustrates the detailed realization of the algorithm.
针对贷款组合优化决策模型的求解问题，论文提出了用于求解该问题的二进制粒子群算法，并阐明了算法的具体实现过程。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
Alternative use of improved particle swarm optimization neural network BP algorithm weight value.
利用改进粒子群算法替代BP算法优化神经网络的权值系数。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
The proposed model is solved by improved particle swarm optimization (PSO) algorithm.
针对此模型，采用改进粒子群优化算法进行求解。
The features and options of Standard test functions, The comparative experiment and the results analyzing of the improved standard particle swarm algorithm and particle swarm optimization ;
标准测试函数的特性与选择，改进粒子群算法与标准粒子群算法的比较实验与结果分析；
The features and options of Standard test functions, The comparative experiment and the results analyzing of the improved standard particle swarm algorithm and particle swarm optimization ;
标准测试函数的特性与选择，改进粒子群算法与标准粒子群算法的比较实验与结果分析；
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