Based on the idea specialization and cooperation, an adaptive Particle Swarm Optimization ( PSO ) algorithm is proposed.
基于粒子分工与合作的思想, 提出一种自适应粒子群优化 ( PSO ) 算法.
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An improved PSO algorithm was presented and applied to optimal the PID parameters of electromotor.
为此,提出一种改进的PSO优化算法,并将该算法应用于电机控制系统的PID参数优化设计.
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Particle Swarm Optimization ( PSO ) for the TSP complete source code, has been tested.
粒子群优化 ( PSO ) 的完整的TSP的源代码, 已经过测试.
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Meanwhile , chaos mutation adopted in order to increase the diversity of particles of PSO.
为有效保证PSO算法的粒子多样性, 在迭代过程中加入混沌变异.
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The contradiction the global exploration and convergence speed particle swarm optimization ( PSO ) algorithm is analyzed.
分析了基本粒子群算法 ( PSO ) 全局搜索能力与收敛速度的矛盾,提出了粒子群相似度的概念.
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This paper presents a new polygonal approximation approach based on the particle swarm optimization ( PSO ) algorithm.
本文提出了一种新的基于粒子群优化算法 ( PSO ) 的多边形近似方法.
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Therefore, we recommend using PSO to solve the optimal solution of the problem model.
因此我们建议使用PSO演 算法来解决模式的最佳解问题.
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PSO and Its Application to Relevance Feedback in Image Retrieval.
粒子群优化算法及其在图像检索中相关反馈上的应用.
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Particls swarm optimization ( PSO ) algorithm is a stochastic global optimization method.
粒子群优化算法是一种全局随机优化算法.
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A cylindricity evaluation approach based on the Particle Swarm Optimization ( PSO ) was proposed.
提出了将粒子群优化算法用于圆柱 度误差 评定的设想.
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A parallel PSO was studied based on the parallel model with controller that is coarse - grain parallel.
现提出了一种基于带控制器并行结构模型的并行微粒群算法,它是一种粗粒度的并行.
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This PSO approach provides a new way to solve constrained problems for the practical applications.
该粒子群算法为实际应用中约束优化问题的求解提供了新的途径.
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A method to evaluate spatial straightness errors adopting particle swarm optimization ( PSO ) is proposed.
摘要提出了一种满足最小区域法的空间直线 度误差 评价的新方法一粒子群算法.
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The improvement nearest neighbor algorithm and cheapest insertion algorithm are combined with PSO as the post - optimization - procedure.
将改进的最邻近插入法和最廉价插入法作为后优化过程与粒子群优化算法结合求解该问题.
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On consideration of the search characteristics of particle swarmabr. PSO , penalty function is assigned dynamically.
针对改进粒子群算法的搜索特点,动态调节罚函数.
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