粒子群优化算法在油气混输管网参数优化中的应用

油气混输管网多目标参数优化问题,一直是优化研究的热点。对这个多目标线性规划问题,采用新出现的粒子群优化算法进行管网优化计算,建立优化计算的目标函数、水力热学模型及管网的分析模型,并用并列选择法和权重系数变化法对模型进行求解。该文尝试把粒子群优化算法应用到油气混输管网参数优化设计中,实验结果表明,采用粒子群算法的优化方法可以节省费用的20%以上。     

基于粒子群算法的最优值求解

粒子群算法是基于群体智能理论的优化算法,广泛应用于多元函数极值求解。本文通过建立基本粒子群算法流程图,应用MATLAB编程求解实例,结果说明粒子群算法计算优于其他优化算法。     

基于自然选择粒子群算法的PID参数优化

PID控制在工业生产中得到广泛的应用,其性能指标取决于PID参数的选取。粒子群算法是一种常见的智能化算法,简单便于实现,文章采取基于自然选择改进的粒子群算法优化PID控制器的参数,与经典的粒子群算法相比较,文章所提出的算法有效地避免了经典粒子群算法过早陷入局部最优的问题,具有较高的求解效率。     

基于粒子群优化算法组卷的研究

研究了考试系统中一个关键的技术——组卷算法,提出了应用粒子群优化算法组卷。PSO算法源于蜜蜂群觅食,是一种智能寻优算法,它首先初始化一群随机粒子（随机解）,然后通过迭代找到最优解。PSO算法组卷是一个离散性求解问题,这是对PSO算法应用上的一个扩展。     

一种新改进的粒子群优化算法

由于粒子群优化算法对多极值复杂问题求解时容易陷入局部极值,提出一种新改进的粒子群优化算法。该改进算法是将粒子群进化过程分为两个不同的阶段,每个阶段应用不同的进化模型,通过结合这两种进化模型的各自优点有效地降低群体陷入局部最优。由仿真实验结果可知,对于复杂多极值函数优化问题,本文算法比标准粒子群优化算法的全局寻优能力更强。     

基于粒子群算法的偏置进近航段多目标优化

为了提高偏置Ⅰ类精密进近飞行程序的设计精度,对其采用改进型的粒子群算法。在求解过程中对粒子群算法进行了约束改进,提出了符合本文的改进粒子群算法,能更好产生初始解,进而处理个体极值和全局极值的选取问题。根据程序设计理论对优化问题构建约束条件,建立多目标分层优化目标函数求解非支配解,最终通过某机场进行验证。     

基于粒子群算法参数优化方案的研究

演化算法被广泛应用于求解NP类组合优化问题。其中粒子群算法因其算法易于实现且效果显著,自其诞生以来就成为研究的焦点。在算法的实际应用过程中会遇到如何兼顾算法收敛高效率和避免过早收敛于局部最优这两种相互博弈的算法优化因素。针对这一问题本文对两种粒子群算法的参数优化方式进行比对,提出了未来算法参数优化的研究方向。     

多种群粒子群分层进化优化算法

粒子群优化算法已越来越广泛的应用于求解优化问题,它简单实用且对一些复杂问题也能得到不错的结果,但它收敛速度慢且容易陷入局部最优值。多种群粒子群分层进化优化算法让粒子分层进化,对于具有不同适应度值的粒子采取不同的进化措施,提高了算法的收敛速度和精度。     

基于改进粒子群算法的电网经济调度问题研究

在电力系统领域,粒子群优化算法(PSO)被广泛应用于实现电能优化、电压控制、以及电容器优化配置等问题。但现有的PSO算法在电力系统优化应用中容易过早收敛,不能得到精确解。文章根据电网最优潮流具有典型的有约束、非线性的特点,提出了一种应用于电网经济调度问题的快速有效的求解方法,即改进粒子群(PSO)算法。从模仿生物遗传进化的角度出发,在参考现有PSO算法的基础上设计一种可以随适应度变化而变化的PSO算法模型。通过该算法与标准PSO算法在IEEE30节点系统上进行算法检验比较,可以清楚地看到所改进的算法在求解电网系统经济调度问题方面所具有的优越性能。     

基于改进粒子群算法的水资源优化配置的研究

针对现有水资源配置模型存在的不精确问题,在现有水资源模型基础上增加了决策偏好系数和排放污染物种类以提高模型精确性,以吉林市水资源基础数据初始化水资源优化配置模型,针对目前对模型进行优化的粒子群算法易出现局部最优等情况,引入萤火虫算法对其进行改进,通过萤火虫趋向最优解的原理改善粒子群算法出现局部最优的情况,并加速其收敛速度。应用改进粒子群算法对模型进行优化求解,得出水资源优化配置方案,以满足经济效益、社会效益、生态环境效益的全面要求。     

基于ε支配的MOGA在0／1背包问题中的应用

多目标遗传算法NSGA—Ⅱ是解决0／1背包问题^[1]的有效算法,但是它还存在一定的缺陷,当0／1背包问题的规模较大时,这种方法很难收敛到Pareto最优边界,因此解的分布性不是很好,解集也很难收敛。针对此问题,提出基于ε支配的MOGA来求解0／1背包问题,通过实验验证该算法在求解分布性上优于NSGA-Ⅱ。     

基于离散粒子群算法的多约束多目标优化

利用粒子群算法(PSO)提出了一个新的粒子编码方法,并将其用于高校排课问题。通过对某高校的排课数据进行测试,结果表明,本文所提出的改进PSO算法对于解决高校排课问题的优化是有效的,对其它多目标问题地求解也有借鉴意义。     

Optimized scheduling of resource-constraints in projects for smart construction

In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning.     

Replicating web contents using a hybrid particle swarm optimization

We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search.     

企业合作创新伙伴的选择研究——基于微粒群算法定权的改进TOPSIS法

选择合作创新是企业实现战略目标及获得技术突破的最优博弈发展策略。合作创新伙伴的评价与选择是企业开展合作创新的首要环节,在一定程度上决定了合作创新的效率和效果。为了科学有效且简单地实现合作创新伙伴的选择,本文将微粒群算法和改进的TOPSIS方法相结合,提出了一种新的有限方案多属性决策方法—基于微粒群算法定权的改进TOPSIS法。通过以与最优和最劣对象距离之和最小为准则,建立一个关于权重的非线性规划问题,给出其数学规划模型,运用微粒群算法求解指标的权重;为克服传统TOPSIS法的不足,基于集对分析联系度的思想,将理想点与负理想点视为确定不确定系统中相互对立的集合,在考察备选方案与理想点或负理想点的联系度时,充分考虑了对立集合的存在,构建了基于联系度的改进TOPSIS法合作创新伙伴的选择综合评价模型;并将其应用于H汽车集团汽车新技术合作创新伙伴选择的实证分析中,验证其科学性和有效性。     

Time-optimal control problem for a linear parameter varying system with nonlinear item

In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm.     

电网故障诊断的二进制人工鱼群算法

电网故障诊断的基本思想是根据保护动作原理将故障诊断问题表示为0-1规划问题。为了保证电网故障诊断的准确性和实时性,提出了一种改进的人工鱼群算法——二进制人工鱼群算法。分析了人工鱼群群聚行为和追尾行为最优方向的前进速度。并在此基础上与遗传算法、粒子群算法和量子免疫算法作了对比分析。结果表明:追尾行为最优方向的前进速度优于群聚行为,二进制人工鱼群算法综合性能优于遗传算法、粒子群算法和量子免疫算法。研究表明二进制人工鱼群算法具有收敛速度快、种群规模小和搜索能力强的特点。     

Optimal location and controller design of STATCOM for power system stability improvement using PSO

The optimal location of a static synchronous compensator (STATCOM) and its coordinated design with power system stabilizers (PSSs) for power system stability improvement are presented in this paper. First, the location of STATCOM to improve transient stability is formulated as an optimization problem and particle swarm optimization (PSO) is employed to search for its optimal location. Then, coordinated design problem of STATCOM-based controller with multiple PSS is formulated as an optimization problem and optimal controller parameters are obtained using PSO. A two-area test system is used to show the effectiveness of the proposed approach for determining the optimal location and controller parameters for power system stability improvement. The nonlinear simulation results show that optimally located STATCOM improves the transient stability and coordinated design of STATCOM-based controller and PSSs improve greatly the system damping. Finally, the coordinated design problem is extended to a four-machine two-area system and the results show that the inter-area and local modes of oscillations are well damped with the proposed PSO-optimized controllers.     

基于粒子群优化算法的结构系统辨识

针对一般智能理论辨识方法在结构系统辨识中存在的问题,提出一种基于粒子群优化算法(PSO)的辨识方法。用粒子群中的粒子表征结构物理参数,以最大似然准则为粒子群优化算法的适应度函数,建立了结构系统的辨识模型。数值仿真分析表明,粒子群优化算法可以精确辨识出结构系统的物理参数。     

应用新型量子粒子群优化算法求解PFSP问题

为了提高粒子群算法在求解调度问题时的搜索能力和优化效率以及避免早熟收敛。通过采用了一种新颖的量子粒子群算法,用量子位的概率幅对粒子位置编码,用量子旋转门实现粒子移动,完成粒子搜索;并采用量子非门来实现变异,从而提高种群多样性。由于每个量子都有两个概率幅,因此每个粒子实际占据两个粒子位置,所以在粒子数目相等的情况下,能加速粒子的搜索进程。仿真实验结果表明,在求解置换流水线生产调度问题时优于基本粒子群算法。