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swarm相关的网络例句

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Particle Swarm Optimization is adopted to detect parameter pattern e.g. circle, ellipse, hyperbola and asymptote.

我们采用一种以粒子群演算法Particle Swarm Optimization 为基础的方法,用来侦测一张影像上的参数图形(如:圆、椭圆、双曲线与双曲线的渐近线)的参数。

Furthermore, the general formula is illustrated as the weighted summation of historical position states, so as to prove that in terms of cumulative iterations, parameters of PSO have an inherent forgetting characteristic in probability, moreover the searching mechanisms of canonical PSO and Barebones particle swarm are almost the same.

通过将粒子运动的一般性描述图解为历史状态加权和的形式,进一步证明了PSO参数随迭代周期的积累,在概率意义上的遗忘特性。分析表明在经过一定周期的搜索后,标准PSO方法同Barebones粒子群方法(Barebones particle swarm, BBPS)具有近似的搜索机制。

This paper applies Particle Swarm Optimization algorithm in the cryptology area to design Boolean function in cryptography.

将粒子群(Particle Swarm Optimization,PSO)算法应用于密码学领域,设计密码学中的密码函数—布尔函数。

Keywords: Economic complex system, Economic system evolution, SWARM, Critical competition, West Region economic development strategy

中文关键词:经济复杂系统,经济系统演化,SWARM,企业临界竞争,西部地区经济发展思路

Through simulating the foraging behavior of the ant society, swarm-intelligence-based routing algorithms can hopefully provide approximate solutions to the above mentioned NPC routing problems.

基于Swarm Intelligence的路由算法是人们的一种新的尝试,它模仿自然界蚂蚁的觅食行为,为寻求具有NPC难度的路由问题的近似解提供了新的思路。

This paper presents a new data clustering algorithm based on glowworm swarm optimization algorithm.

摘要 本研究以萤火虫最佳化(Glowworm Swarm Optimization, GSO)演算法为基础,发展出新的资料分群方法,我们称之为CGSO。

The concept of digital life was first presented in computer simulation experiment by Thomas.S.Ray. Based on it, the theory knowledges were introduced such as complex adaptive system and Agent model-building. Then two kinds of different digital lives and their coexistence environment were constructed. Nature lives' most foundational biological phenomenas like reproduction and annihilation were simulated. The interactive behavior between individual and community was observed by visualization windows. Finally, the inscrutability and emergence of the results were attained, and the simulation for two digital lives with Swarm was accomplished.

数字生命的概念是托马斯雷最早在计算机上模拟实验提出的,以此为指导,介绍了复杂适应系统、Agent建模等理论知识,利用Swarm仿真平台构建了两种异类数字生命及共同生存的环境,模拟自然界生命体最基础的繁殖歼灭等生命现象,通过可视化窗口观察生命体个体及群体间的交互行为,得到结果的不可测性和突现性,实现了两种数字生命的Swarm仿真。

In: Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press, 1999: 1958~1962.[9] Kennedy J. Small worlds and Mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press, 1999: 1931~1938.[10] Peram T, Veeramachaneni K, Mohan C K. Fitness-distance-ratio based particle swarm optimization.

近来性能较为显著的基于PSO算法的改进算法有Thanmaya Peram[10]等人于2003年提出的基于粒子群优化的适应值-距离-比例算法(Fitness-Distance-Ratio based Particle Swarm Optimization, FDR-PSO),在此算法中每个粒子根据一定的适应值-距离-比例原则,向附近具有较好适应值的多个粒子进行不同程度的靠近,而不仅仅只向当前所发现的最好粒子靠近。

To avoid the prematurity phenomenon of particle swarm optimization, a novel hybrid genetic particle swarm optimization algorithm is proposed. An eliminative mechanism is introduced into the iteration of PSO.

针对粒子群算法容易出现&早熟&的缺点,提出了一种改进的混合遗传粒子群(hybrid genetic particle swarm optimization, HGPSO)算法。

Firstly, on the basis of defining the characteristics of project and reviewing the mathematical models for RCPSP, we constructed the mathematical model for RCPSP under the condition that there is non-fixed duration and splittable tasks in project; In order to solve above model, we emphatically discussed the basic principle of the swarm intelligence algorithm--the particle swarm optimization algorithm as well as improvement research for current algorithm, and then proposed a new improved particle swarm optimization algorithm and analyzed its application process in the resource-constrained project scheduling problem; Lastly, in order to confirm the validity of the model and the algorithm, we took an experiment based on 108 instances that produced by problem instance generator which was developed by our team.

首先,在对RCPSP项目特征进行界定以及对项目进度优化模型回顾的基础之上,构建了基于活动工期不固定、任务可拆分特征的RCPSP的数学模型;为了求解上述模型,本文详细阐述粒子群优化算法(Particle Swarm Optimization,PSO)的基本原理以及当前对其改进的研究成果,提出了一种新的改进PSO算法,并对其在RCPSP的应用过程进行了设计;最后,为了检验模型的有效性及算法的优越性,基于梯队内部开发的案例生成器生成的108个算例,进行了仿真试验。

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