• Three improved particle swarm optimization algorithms are proposed.

    提出了三种改进的微粒群算法。

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  • 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。

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  • 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。

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  • The improved particle swarm optimization (PSO) is used to optimize the PID controller parameters.

    应用改进的粒子群优化算法优化PID参数。

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  • Aimed at PSO's defect of prematurity, an improved particle swarm optimization (IPSO) is presented.

    针对粒子群算法本身存在早熟的不足,提出了一种改进的粒子群优化算法(IPSO)。

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  • 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。

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  • 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。

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  • 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。

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  • Improved Particle Swarm Optimization is applied to short-term optimal scheduling of the cascade hydropower plants.

    应用改进粒子群算法求解梯级水电站短期优化调度问题。

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  • An improved particle swarm optimization neural network for automatic detection fertility of hatching eggs is presented.

    提出了一种基于改进粒子群神经网络进行孵化种蛋成活性自动检测的方法。

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  • 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。

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  • On this basis, improved particle swarm optimization based on the magnetic bearing neural network PID control scheme is proposed.

    在此基础上,提出基于改进粒子群算法优化的磁轴承神经网络PID控制方案。

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  • At last, the thesis predicts the application prospect of the improved particle swarm optimization and gives some directions of future research.

    最后,本文对于改进粒子群算法的应用前景进行了展望,并给出了进一步的研究方向。

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  • Secondly, the improved particle swarm optimization for flying robot for overhead powerline inspection is used to resolve the global path planning issue.

    其次,采用一种基于改进粒子群算法的路径规划方法实现了飞行机器人的全局路径规划方法。

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  • 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。

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  • 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。

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  • 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。

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  • The economic dispatch of a small microgird example in island mode was optimized by improved particle swarm optimization PSO method, simulation results show the effectiveness of the proposed method.

    采用改进粒子群算法对孤岛运行模式下的一个小型的微网系统算例进行研究,仿真计算结果表明了所提方法的有效性。

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  • 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。

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  • A method based on improved binary particle swarm optimization (PSO) is proposed for distribution network reconfiguration with the objective of load balancing.

    提出了基于改进的二进制粒子群优化算法、以均衡负荷为目标的配电网重构方法。

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  • As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.

    为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷,提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。

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  • Aiming at the stagnation problem of the cooperative particle swarm optimization, this paper presents an improved cooperative particle swarm optimization.

    针对协同粒子群优化算法存在的停滞现象,提出了一种改进的协同粒子群优化算法。

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  • The particle swarm optimization speeded up the evolution process, and improved the convergence speed and accuracy.

    通过这种处理使得粒子群体的进化速度加快,从而提高了算法的收敛速度和精度。

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  • An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR).

    为了增强情感识别过程中皮肤电反应(GSR)信号特征选择的有效性,提出了一种改进的模拟退火免疫粒子群算法。

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  • 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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  • 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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