改进的粒子群优化算法在结构优化中的应用

结构优化设计对于实际工程具有重要的意义。本文通过对标准的粒子群优化算法进行分析,将惯性权重系数引入到原算法中,并建立了相应的优化模型。介绍了改进的粒子群算法的基本思想、结构优化模型及其实施的具体步骤,最后通过算例及与标准PSO优化算法的比较,验证了改进后的方法的效率和有效性。结果表明该方法提高了优化性能,具有很好的应用前景。     

粒子群优化算法在离散变量结构优化中的应用

介绍了用于离散变量的粒子群优化(PSO)算法以及加入了约束处理的启发式粒子群优化(HPSO)算法.将HPSO算法的约束处理策略与另一种适用于粒子群算法的约束处理方法结合,并将改进后的算法应用到3个离散变量桁架结构截面优化设计算例中,同时与HPSO算法进行了对比分析.对于每个算例,改进算法和HPSO算法都运行了多次,从多次运行的统计数据中可以看出,改进算法比HPSO算法更稳定、收敛速度更快、搜索精度更高,且其约束处理方法减少了结构分析的次数,从而提高了整个程序运行的速度.     

改进粒子群算法在电力系统无功优化中的应用

为研究解决电网无功优化问题的可行算法[1],对粒子群算法进行探讨和改进,引入共轭梯度法,克服标准粒子群算法容易进入局部收敛、收敛精度不高等缺点[2]。改进的粒子群算法寻优质量高,收敛速度快,节点电压满足系统运行要求,系统网损较小,是解决电力系统无功优化的高效可行算法。     

粒子群算法及其优化方案

粒子群算法作为一种智能优化算法,由于其实现简单,易于与其他方法结合,在现实应用中逐渐显示出强大的优势。本文首先介绍了基本的粒子群算法,分析了粒子群算法的优化方面,并且对不同的优化方面进行详细分析。     

粒子群优化算法在结构参数识别中的应用

利用PSO算法将系统识别问题转化为高维多模优化问题,进行结构参数识别的研究。PSO算法是一种新颖的随机搜索进化算法,通过采取全局优化的策略确保算法得到优化问题的最优解。在输入输出数据不完备且含真实的噪声污染,以及系统质量、刚度等先验信息又缺乏的情况下,利用将基于该算法的识别方法应用于一个真实结构,验证基于PSO算法的识别方法在真实结构系统识别中的有效性。     

改进的群搜索优化算法及其应用

群搜索(GSO)算法是一种新的群智能优化算法,适用于结构优化设计.本文通过对GSO算法进行改进,简化了算法的计算过程,提高了优化性能.对算法的改进主要有二个方面:一是采用随机搜索,放弃了按角度搜索的方式;二是在生成个体新位置时,增加了一个随迭代次数递减的控制变量-分量变异概率,用于限制允许变异的维的数量.通过对经典桁架算例的优化以及与标准GSO算法的计算结果比较,可以看出改进后的群搜索优化算法(SGSO)具有更好的收敛速度和收敛精度,SGSO算法的结构比GSO算法更简单、易于实现并且计算用时更少.     

粒子群优化算法在桁架优化设计中的应用

粒子群优化（PSO）算法是近年来发展起来的一种基于群智能的随机优化算法,具有概念简单、易于实现、占用资源低等优点。为了解决有应力约束和位移约束的桁架的尺寸优化问题,将PSO算法应用于桁架结构的尺寸优化设计。首先介绍了原始的PSO算法的基本原理,然后引入压缩因子改进了PSO算法,并提出合理的参数设置值。对几个经典问题进行了求解,并与传统的优化算法和遗传算法进行了比较。数值结果表明,改进的PSO算法具有良好的收敛性和稳定性,可以有效地进行桁架结构的尺寸优化设计。     

改进的多目标启发式粒子群算法及其在桁架结构设计中的应用

针对工程结构多目标优化设计中出现的约束条件处理能力差、编程复杂,计算效率低且收敛精度差等问题,对启发式粒子群算法(HPSO)进行改进,提出了多目标启发式粒子群算法(MOHPSO),并与多目标粒子群算法(MOPSO)和改进的多目标群搜索算法(IMGSO)进行比较。通过对15杆平面桁架、40杆平面桁架和72杆空间桁架3个经典算例的计算,证明了所提出的MOHPSO算法的有效性。结果表明:MOHPSO算法具有收敛精度高、约束处理能力强、全局最优解选取更合理、非劣解集维护效率高等特点。     

一种基于翻转角度的二进制粒子群优化算法

为了提高二进制粒子群优化的收敛速度和收敛效果,提出一种新的改进二进制粒子群优化算法,该算法利用翻转角度作为进化条件,利用局部翻转因子和全局翻转因子来决定粒子群的收敛速度。实验证明,该方法具有更快的收敛速度和更佳收敛值。     

混沌模拟退火粒子群优化算法

基于模拟退火思想的粒子群优化算法和混沌粒子群优化算法,提出了混沌模拟退火粒子群优化算法,编写了其具体流程图,并通过两个算例,验证了该算法的效率和有效性,结果表明该方法可行,具有广泛的应用前景。     

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.     

粒子群-鱼群混合算法在桁架结构优化设计中的应用

群智能算法由于其优异的搜索性能被广泛应用于结构优化设计,人工鱼群算法和粒子群算法都是基于动物群体行为的智能优化随机算法.本文介绍了人工鱼群算法和粒子群算法的基本原理,并提出了粒子群和鱼群杂交混合的一种新方法:粒子群-鱼群混合算法,将粒子群-鱼群混合算法应用到四个桁架结构的重量优化设计,包括平面桁架结构和空间桁架结构,通过比较粒子群-鱼群混合算法、人工鱼群算法、粒子群算法的优化结果,发现改进的粒子群-鱼群混合算法具有收敛精度高、收敛速度快等特点,同时具有较好的稳定性,可用于结构优化设计.     

Design optimization of tall steel buildings by a modified particle swarm algorithm

Optimal design of tall buildings, as large‐scale structures, is a rather difficult task. To efficiently achieve this task, the computational performance of the employed standard meta‐heuristic algorithms needs to be improved. One of the most popular meta‐heuristics is particle swarm optimization (PSO) algorithm. The main aim of the present study is to propose a modified PSO (MPSO) algorithm for optimization of tall steel buildings. In order to achieve this purpose, PSO is sequentially utilized in a multi‐stage scheme where in each stage an initial swarm is generated on the basis of the information derived from the results of previous stages. Two large‐scale examples are presented to investigate the efficiency of the proposed MPSO. The numerical results demonstrate the computational advantages of the MPSO algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

太阳影子定位参数反演模型的混合粒子群算法

针对非线性太阳影子定位参数反演问题,给出一种基于单纯形算法的权重线性递减的粒子群混合算法,此混合算法融合了单纯形算法的精确局部搜索能力和粒子群算法全局搜索能力,且采用权重线性递减的粒子群算法加快了算法的收敛速度。在太阳影子定位参数反演的数值实验中,所提算法不仅成功反演参数,而且拥有较高的计算精度和更快的收敛速度。     

基于离散粒子群算法多旋翼无人机任务分配研究

应用离散粒子群算法解决消防救援队伍在现场使用多旋翼无人机时存在的多任务分配难题。通过分析现场任务分配条件，在评价指标设计上综合考虑续航、探测宽度、探测气体种类、任务时间以及任务时序约束等问题，并引入逆转算子对离散粒子群算法进行优化。仿真结果表明，优化的粒子群算法相比遗传算法具有更好的寻优性和收敛性。上述研究可以为灭火救援现场多旋翼无人机任务分配提供一定的算法支持。     

基于微粒群算法和模糊神经网络的火灾探测研究

针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。     

微粒群优化算法在Theis公式参数识别中的应用

采用一种微粒群优化算法来识别承压完整井非稳定地下水运动Theis公式中的水文地质参数。微粒群算法是一种新型的群体智能算法,它将每个个体看作在多维搜索空间中的一个没有重量和体积的微粒,并在搜索空间中以一定的速度飞行,该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。然后根据个体适应值大小运算,根据适应度函数对微粒的速度和位置进行进化,最终得到足够好的适应度值。本文采用微粒群算法可根据抽水试验资料快速反演Theis公式近似解析解中的水文地质参数。实例计算结果表明该微粒群算法计算速度快,在水文地质逆问题求解中值得推广应用。     

A novel approach for optimal chiller loading using particle swarm optimization

This study employs two new methods to solve optimal chiller loading (OCL) problem. These methods are continuous genetic algorithm (GA) and particle swarm optimization (PSO). Because of continuous nature of variables in OCL problem, continuous GA and PSO easily overcome deficiencies in other conventional optimization methods. Partial load ratio (PLR) of the chiller is chosen as the variable to be optimized and consumption power of the chiller is considered as fitness function. Both of these methods find the optimal solution while the equality constraint is exactly satisfied. Some of the major advantages of proposed approaches over other conventional methods can be mentioned as fast convergence, escaping from getting into local optima, simple implementation as well as independency of the solution from the problem. Abilities of proposed methods are examined with reference to an example system. To demonstrate these abilities, results are compared with binary genetic algorithm method. The proposed approaches can be perfectly applied to air-conditioning systems.     

Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

The response of rocks to stress can be highly non-linear, so sometimes it is difficult to establish a suitable constitutive model using traditional mechanics methods. It is appropriate, therefore, to consider modeling methods developed in other fields in order to provide adequate models for rock behavior, and this particularly applies to the time-dependent behavior of rock. Accordingly, a new system identification method, based on a hybrid genetic programming with the improved particle swarm optimization (PSO) algorithm, for the simultaneous establishment of a visco-elastic rock material model structure and the related parameters is proposed. The method searches for the optimal model, not among several known models as in previous methods proposed in the literatures, but in the whole model space made up of elastic and viscous elementary components. Genetic programming is used for exploring the model's structure and the modified PSO is used to identify parameters (coefficients) in the provisional model. The evolution of the provisional models (individuals) is driven by the fitness based on the residual sum of squares of the behavior predicted by the model and the actual behavior of the rock given by a set of mechanical experiments. Using this proposed algorithm, visco-elastic models for the celadon argillaceous rock and fuchsia argillaceous rock in the Goupitan hydroelectric power station, China, are identified. The results show that the algorithm is feasible for rock mechanics use and has a useful ability in finding potential models. The algorithm enables the identification of models and parameters simultaneously and provides a new method for studying the mechanical characteristics of visco-elastic rocks.