基于PPSO的精馏塔模型参数闭环辨识

针对粒子群优化算法(particle swarm optimization,PSO)在高维空间复杂曲面寻优时易于陷入局部最小值的问题,分组扰动粒子群优化算法(partially-perturbed particle swarm optimization,PPSO)结合问题特征,采用启发式规则,实施参数分组扰动策略,对PSO算法进行改进,从而增大了跳出局部极小的可能性。本文主要研究PPSO在精馏塔模型参数闭环辨识上的应用,分别针对模型参数可辨识性,参数的不同分组,鲁棒性进行分析验证;并在其他精馏塔模型上进行了相应的验证。仿真实验表明,PPSO辨识算法比序列近似法等其它辨识算法具有更高的辨识精度,并且具有较强的鲁棒性;在其他精馏塔模型参数辨识上PPSO算法也同样取得了很好的辨识精度。实验结果证明了PPSO算法在精馏塔模型参数闭环辨识上的可行性和有效性。     

基于局部协同与竞争变异的动态多种群粒子群算法

针对粒子群算法在处理复杂优化问题时,出现多样性较差、收敛精度低等问题,提出了基于局部协同与竞争变异的动态多种群粒子群算法(Dynamic Multi-population Particle Swarm Optimization Based on Local Cooperative and Competitive M utation,LC-DM PPSO).LC-DM PPSO算法设计了一种局部协同的方法,该方法划分种群成多个子种群,划分后的子种群再通过非支配排序、差分变异的方法选择出一对领导粒子.同时,对粒子的更新方法进行改进,让各个目标优化更加均衡,增强LC-DM PPSO算法的局部搜索能力,提高收敛精度.在LC-DM PPSO算法中,为了防止出现"早熟"收敛的情况,引入竞争变异来增加种群多样性.最后,通过选择一系列标准测试函数将LC-DM PPSO算法与3种进化算法进行比较,验证所提算法的有效性.实验结果显示,所提算法的多样性和收敛性比其他3种进化算法更好,优化效果更佳.     

蚁群算法参数优化

针对蚁群算法运行参数选取问题,提出一种利用粒子群优化算法对蚁群算法的运行参数进行优化选择的方法。将蚁群算法的运行参数作为粒子群的位置信息,在算法迭代过程中使用粒子的当前位置作为算法参数,运行蚁群算法求解标准优化问题,设计适应值评价函数对求解性能做出评价,引导粒子向着适应值高的方向趋近。仿真结果表明,该算法能够方便有效地实现对蚁群算法运行参数的优化选取。     

目(2055)基于聚类的多子群粒子群优化算法*

在粒子群优化算法基础上,提出了基于聚类的多子群粒子群优化算法。该算法在每次迭代过程中首先通过聚类方法把粒子群体分成若干个子群体,然后粒子群中的粒子根据其个体极值和“子群”中的最优粒子更新自己的速度和位置值。这种处理增加了粒子之间的信息交换,利用了更多粒子在迭代过程中的信息,使算法的收敛性能更好。仿真结果表明,该算法的性能优于粒子群优化算法。     

基于改进PSO算法的供应链优化

在建立供应链优化模型和分析基本粒子群优化算法的基础上,提出了一种求解供应链优化问题的改进粒子群算法。在优化过程中,该算法以优良适应值粒子取代部分不良适应值粒子,使算法具有过滤能力,加快了搜索速度,并保证了收敛于全局最优解。实验结果与基本粒子群算法进行了验证和比较,表明该改进粒子群算法具有较好的性能和简单快速准确等特点。     

基于最优变异的粒子群优化算法

为了提高粒子群优化算法的性能,提出了一种带最优变异的改进粒子群优化算法。该算法的惯性权值满足不同粒子对全局和局部搜索能力的不同需求,每次迭代后根据适应度值会作相应的调整,在搜索过程中所引入的变异算子将对粒子群中最优粒子进行变异,以防止算法早熟收敛。对4个典型的测试函数的仿真表明,该算法比标准粒子群优化算法有更好的收敛性和更快的收敛速度。     

基于改进粒子群优化算法的多用户检测器

提出了一种基于改进粒子群优化算法的多用户检测器。介绍了最佳多用户检测模型以及粒子群优化算法的基本思想。进行了理论依据和仿真性能分析。仿真结果表明:该检测器在误码率性能和抗“远近”效应上优于传统检测器和基于粒子群优化得多用户检测器,计算复杂度较低。     

基于Sigmoid惯性权值的自适应粒子群优化算法

针对粒子群优化算法存在的缺点,提出了基于Sigmoid惯性权值的自适应粒子群优化算法。一方面,引入粒子群早熟收敛的计算公式,以指导算法在进化过程中的具体执行策略,有效避免计算的盲目性,加快算法的收敛速度;另一方面,通过设定粒子群聚集程度的判定阈值,以使算法在线性递减惯性权值和基于Sigmoid函数思想的非线性递减惯性权值之间进行自适应地动态调整,从而有效减少了算法陷入局部最优的可能。测试函数仿真结果表明了该算法的可行性和有效性。     

混合无线传感器网络节点覆盖优化

为了解决混合无线传感器网络的节点覆盖率低的问题,提出了改进粒子群的混合无线传感器网络节点覆盖迭代优化算法.在该算法中,首先将混合无线传感器网络节点覆盖模型转化为在网络系统中动态的求覆盖率最大值的节点部署位置寻优问题;然后提出利用改进粒子群算法对节点覆盖优化方案进行粒子及其权值映射,并依据粒子粒距聚类度和粒子信息熵对粒子权值进行调整,再依据粒子适应度值对粒子局部最优值和全局最优值进行更新;最后迭代地对粒子的位置和速度进行计算,输出具有最优覆盖率的节点部署方案.仿真结果证明,该算法能够有效的提升网络覆盖率,且算法的收敛速度快.     

基于改进粒子群优化算法的多用户检测器

提出了一种基于改进粒子群优化算法的多用户检测器。介绍了最佳多用户检测模型以及粒子群优化算法的基本思想。进行了理论依据和仿真性能分析。仿真结果表明：该检测器在误码率性能和抗“远近”效应上优于传统检测器和基于粒子群优化得多用户检测器，计算复杂度较低。     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

一种改进的多目标粒子群优化算法及其应用

为提高多目标粒子群优化 (MOPSO)算法处理多目标优化问题的性能, 降低计算复杂度, 改善算法的收敛性, 提出了一种改进的多目标粒子群优化算法。通过运用比例分布及跳数改进机制策略的方法, 使该算法不仅继承了MOPSO算法的优点, 而且具有很强的局部搜索能力和较好的鲁棒性能, 使非劣解集均匀分布, 尽可能逼近真实的非劣前沿。通过对多连杆悬架空间结构硬点的多目标优化, 进一步验证了该算法的实用性及其优越性。     

基于混沌搜索的粒子群优化算法

粒子群优化算法(PSO)是一种有效的随机全局优化技术。文章把混沌优化搜索技术引入到PSO算法中,提出了基于混沌搜索的粒子群优化算法。该算法保持了PSO算法结构简单的特点,改善了PSO算法的全局寻优能力,提高的算法的收敛速度和计算精度。仿真计算表明,该算法的性能优于基本PSO算法。     

An optimization algorithm employing multiple metamodels and optimizers

Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.     

基于DCS的电力节能优化控制系统的设计与实现

为了解决电力系统的节能优化问题,本文在传统的PSO节能控制方法的基础上,提出了一种多重自适应的粒子群优化算法,应用分散控制系统设计与实现了一种新的电力节能优化控制系统。数值仿真的结果说明了使用所提出的粒子群算法的基于DCS的电力节能优化控制系统在电力调度最佳节点的搜索精确度要高于相同条件下的一般的电力控制系统。使用所提算法的电力节能优化控制系统,能有效地对电力能耗进行优化,且具有较高的实用性。     

基于改进粒子群算法的变异体选择优化

变异测试是常用的测试方法之一,变异测试分析的过程中计算开销会比较大,问题主要集中于测试过程中会产生大量的变异体,为了减少变异体的数量,提出用标准粒子群聚类算法进行选择优化,但标准粒子群算法在被测数据量增加到一定数量的时候,它的迭代次数就会增加、收敛速度就会下降。针对以上问题提出基于改进的粒子群算法对变异体进行选择优化。通过对变异体集合进行聚类分区,增强变异体集合的多态性,从而对粒子群算法改进优化。实验结果表明在不影响测试充分度的前提下,使变异体的数量大幅度减少,同时与K-means算法以及标准粒子群算法相比之下,改进后的方法具有更好的优化效果。     

Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization

In the present paper, particle swarm optimization, a relatively new population based optimization technique, is applied to optimize the multidisciplinary design of a solid propellant launch vehicle. Propulsion, structure, aerodynamic (geometry) and three-degree of freedom trajectory simulation disciplines are used in an appropriate combination and minimum launch weight is considered as an objective function. In order to reduce the high computational cost and improve the performance of particle swarm optimization, an enhancement technique called fitness inheritance is proposed. Firstly, the conducted experiments over a set of benchmark functions demonstrate that the proposed method can preserve the quality of solutions while decreasing the computational cost considerably. Then, a comparison of the proposed algorithm against the original version of particle swarm optimization, sequential quadratic programming, and method of centers carried out over multidisciplinary design optimization of the design problem. The obtained results show a very good performance of the enhancement technique to find the global optimum with considerable decrease in number of function evaluations.     

Sequential optimization using multi-level cokriging and extended expected improvement criterion

To reduce the computational cost of metamodel based design optimization that directly relies on the computationally expensive simulation, the multi-fidelity cokriging method has gained increasing attention by fusing data from two or more models with different levels of fidelity. In this paper, an enhanced cokriging based sequential optimization method is proposed. Firstly, the impact of considering full correlation of data among all models on the hyper-parameter estimation during cokriging modeling is investigated by setting up a unified maximum likelihood function. Then, to reduce the computational cost, an extended expected improvement function is established to more reasonably identify the location and fidelity level of the next response evaluation based on the original expected improvement criterion. The results from comparative studies and one airfoil aerodynamic optimization application show that the proposed cokriging based sequential optimization method is more accurate in modeling and efficient in model evaluation than some existing popular approaches, demonstrating its effectiveness and relative merits.     

基于改进自适应粒子群算法的目标定位方法

针对现有目标定位求解算法推导复杂和自适应粒子群算法仍存在收敛速度慢、计算量大的缺点,提出了一种基于速度自适应和变异自适应融合的改进粒子群算法。该算法在速度自适应粒子群算法的基础上,优化选择粒子,并根据种群适应度方差值进行自适应变异,增强算法快速收敛的能力。仿真结果表明该方法能有效地提高目标定位精度,在随机噪声干扰方差为。.5的条件下,定位均方误差不超过1. 5m,且收敛速度增快,计算量减小。     

Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel

This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks is proposed to accurately predict the time history responses of structures. The metamodel proposed is called fuzzy self-organizing radial basis function (FSORBF) networks. In this study, the most influential natural periods on the dynamic behavior of structures are treated as the inputs of the neural networks. In order to find the most influential natural periods from all the involved ones, ANFIS is employed. To train the FSORBF, the input–output samples are classified by a hybrid algorithm consisting of SA and SOM clusterings, and then a RBF network is trained for each cluster by using the data located. In the second strategy, particle swarm optimization (PSO) is employed to find the optimum design. Two building frame examples are presented to illustrate the effectiveness and practicality of the proposed methodology. A plane steel shear frame and a realistic steel space frame are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology.