提出了三种改进的微粒群算法。
Three improved particle swarm optimization algorithms are proposed.
微粒群算法的理论分析一直是其研究的难点。
Theory analysis of particle swarm optimization is always an important research topic.
微粒群算法的研究领域不断扩大，也不断深入。
本文提出了一种新的求解约束优化问题的微粒群算法。
The HPSO combines the particle swarm optimization with constraint optimization and direct search.
提出了一种适用于高维数值优化问题的空间分割微粒群算法。
Space division particle swarm optimization is proposed to solve the high-dimensional numerical optimization problem in this paper.
微粒群算法共包括3部分：第一部分为微粒目前飞行的状态；
Particle swarm optimization (PSO) include 3 parts: the first part was the current flight status for the particles;
首先，研究了一种新的基于微粒群算法的模糊滑模控制方法。
Firstly, a new fuzzy sliding mode controller method is proposed for a class of nonlinear system based on PSO.
实验结果表明该改进微粒群算法可以有效地解决高维数值优化问题。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
实验结果表明，该并行算法的性能比标准微粒群算法有了很大的提高。
The experimental results show that the performance of the proposed parallel algorithm is better than that of the standard PSO.
微粒群算法是一种新型的进化计算方法，已在许多领域得到了广泛的应用。
Particle swarm optimizer (PSO) is a new evolutionary computation method, which has been successfully applied to many fields.
提出了一种基于微粒群算法(PSO)的图像增强方法，把图像增强看作最优化问题。
In this study, a Particle Swarm optimization (PSO) approach to image enhancement is proposed, in which image enhancement is formulated as an optimization problem.
微粒群算法是一种新型的智能优化算法，有较强的发现最优解的能力，且算法简单，易于实现。
Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement.
受自然界共生现象的启发，将微粒群算法和协同进化相结合，提出了一种多微粒群协同进化算法。
Illumined by phenomenon of co-evolution in nature, particle swarm optimization is combined with co-evolution, a multi-particle swarm co-evolution algorithm is presented.
本文提出一个求解约束工程设计问题的新的混合算法—与可行基规则相结合的局部收缩微粒群算法。
This paper the hybrid local constriction particle swarm optimization (HLPSO) with a feasibility-based rule is proposed to solve constrained engineering design problems.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
最后，通过对基于行为学和微粒群算法的路径规划方法的MATLAB仿真，验证了本文算法的有效性。
At last, validate the effectiveness of the algorithm by simulation of the Path Planning algorithm base on Behavior and Particle Swarm Optimization in MATLAB.
目前微粒群算法已广泛应用于函数优化、神经网络训练、数据挖掘、模糊系统控制以及其他的应用领域。
Recently, Particle Swarm optimization is applied into function optimization, Neural Networks, data mining, Fuzzy Control System and other application field.
与标准微粒群算法相比，算法的全局搜索能力和收敛速度都得到了显著提高，同时能够有效避免早熟收敛。
Compared to standard PSO, its global searching ability and the speed of convergence is significantly improved, and the premature convergence problem is effectively avoided.
该算法通过将整个搜索空间分割成若干子空间，在这些子空间上利用嵌入零搜索算子的微粒群算法进行优化。
The algorithm divides the whole search space into a number of sub-spaces, which are optimized by utilizing the PSO embedded zero search operators.
在离散微粒群算法的基础上，提出了一种基于二进制的随机多目标P SO算法，并对感知模型进行覆盖优化。
Then a binary awareness model based on stochastic sensor placement and a stochastic multi-objective PSO arithmetic based on binary system which is applied on awareness model have been present.
以上述模型为基础，以毁伤概率为优化目标，采用微粒群算法（PSO）对该系统待优化的各参量进行综合优化。
To optimize the damage probability, the particle swarm optimization algorithm (PSO) is adopted to optimize the system parameters, based on above models.
实验结果表明：若选择合适的通讯周期时，该并行微粒群算法不仅具有理想的加速比，而且有效地提高解的质量。
The experiment results show that if the period of communication is selected appropriate, this parallel PSO not only has perfect speedup, but also improves the quality of result.
针对这一问题，分析了海军维修保障系统结构，建立了维修保障系统模型，采用微粒群算法对模型进行智能优化。
Aiming at this problem, naval maintenance system structure is analyzed, maintenance system model is established, PSO is used to optimize the model.
计算结果表明，微粒群算法具有概念简单、容易实现、计算速度快等优点，适合应用于面板堆石坝坝料参数反演分析。
The results indicate the validity of improved PSO methods in the back analysis of the mechanical parameters of concrete face rock-fill dam.
同时通过微粒群算法，解决了智能鱼群体运动模式单一的问题。更合理、有效地模拟了自然鱼的生命特征和智能行为。
Meanwhile, the thesis USES the PSO to modify the movement patterns of the intelligent fish which can improve the natural characteristics of the artificial fish and intelligent behavior.
以目标搜索定位任务为背景，用扩展微粒群算法对具有非完整运动约束特性的自主移动轮式机器人组成的群机器人系统实施协调控制。
Taking target search with swarm robots for instance, we explore an approach to control swarm whose members are autonomous wheeled mobile robots with non-holonomic constraints.
以目标搜索定位任务为背景，用扩展微粒群算法对具有非完整运动约束特性的自主移动轮式机器人组成的群机器人系统实施协调控制。
Taking target search with swarm robots for instance, we explore an approach to control swarm whose members are autonomous wheeled mobile robots with non-holonomic constraints.
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