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关键词

优化 18

多目标优化 9

优化设计 8

遗传算法 6

稳健设计 4

不确定性 3

动态规划 3

一阶分析法 2

五品联动 2

仿真优化 2

信息素 2

分布式优化 2

参数率定 2

可靠性灵敏度 2

粒子群优化 2

粒子群优化算法 2

群体智能 2

非线性 2

1)幂模型 1

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Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

Syamasree BISWAS(RAHA), Kamal Krishna MANDAL, Niladri CHAKRABORTY

《能源前沿（英文）》 2013年第7卷第2期页码 174-181 doi: 10.1007/s11708-013-0246-x

摘要： The reactive power dispatch (RPD) problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE 118-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study.

关键词： real power loss minimization voltage stability constriction factor particle swarm optimization (PSO)

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Estimation of distribution algorithm enhanced particle swarm optimization for water distribution networkoptimization

Xuewei QI,Ke LI,Walter D. POTTER

《环境科学与工程前沿（英文）》 2016年第10卷第2期页码 341-351 doi: 10.1007/s11783-015-0776-z

摘要： The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M?) than the current literature reported best minimum (1.923M?). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.

关键词： particle swarm optimization (PSO) diversity control estimation of distribution algorithm (EDA) water distribution network (WDN) premature convergence hybrid strategy

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Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm

K. MURALI,T. JAYABARATHI

《能源前沿（英文）》 2014年第8卷第3期页码 290-296 doi: 10.1007/s11708-014-0305-y

摘要： This paper proposes application of a catfish particle swarm optimization (PSO) algorithm to economic dispatch (ED) problems. The ED problems considered in this paper include valve-point loading effect, power balance constraints, and generator limits. The conventional PSO and catfish PSO algorithms are applied to three different test systems and the solutions obtained are compared with each other and with those reported in literature. The comparison of solutions shows that catfish PSO outperforms the conventional PSO and other methods in terms of solution quality though there is a slight increase in computational time.

关键词： economic dispatch (ED) valve point loading catfish particle swarm optimization (PSO) optimization

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Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

K Sathish KUMAR,S NAVEEN

《能源前沿（英文）》 2014年第8卷第4期页码 434-442 doi: 10.1007/s11708-014-0313-y

摘要： One of the very important ways to save electrical energy in the distribution system is network reconfiguration for loss reduction. Distribution networks are built as interconnected mesh networks; however, they are arranged to be radial in operation. The distribution feeder reconfiguration is to find a radial operating structure that optimizes network performance while satisfying operating constraints. The change in network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally opened) switches of the network. These switches are changed in such a way that the radial structure of networks is maintained, all of the loads are energized, power loss is reduced, power quality is enhanced, and system security is increased. Distribution feeder reconfiguration is a complex nonlinear combinatorial problem since the status of the switches is non-differentiable. This paper proposes a new evolutionary algorithm (EA) for solving the distribution feeder reconfiguration (DFR) problem for a 33-bus and a 16-bus sample network, which effectively ensures the loss minimization.

关键词： distribution system reconfiguration (DFR) power loss reduction catfish particle swarm optimization (catfish PSO) radial structure

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A hybrid BFA-PSO algorithm for economic dispatch with valve-point effects

T. Jayabarathi, Prateek Bahl, Harsha Ohri, Afshin Yazdani, V. Ramesh

《能源前沿（英文）》 2012年第6卷第2期页码 155-163 doi: 10.1007/s11708-012-0189-7

摘要： This paper presents a novel and efficient method for solving the economic dispatch (ED) problems with valve-point effects, by integrating the biased velocity of particle swarm optimization (PSO) to the chemotaxis, swarming and reproduction steps of bacterial foraging algorithm (BFA). To include valve point effects sinusoidal terms are added to the fuel cost function. This makes the ED problems highly non-linear. In order to solve such problems the best cell (or particle) biased velocity (vector) is added to the random velocity of the BFA to reduce randomness in movement (evolution) and to increase swarming. This results in the hybrid bacterial foraging algorithm (HBFA). To demonstrate the effectiveness of the proposed HBFA method, numerical studies have been performed for three different sample systems. Comparison of the results obtained by the HBFA with the BFA and other evolutionary algorithms clearly show that the proposed method outperforms other methods in terms of convergence rate and solution quality in solving the ED problems with valve-point effects.

关键词： bacterial foraging algorithm (BFA) economic dispatch (ED) particle swarm optimization (PSO) valve-point effects

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Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

《环境科学与工程前沿（英文）》 2023年第17卷第8期 doi: 10.1007/s11783-023-1698-9

摘要：

● Data acquisition and pre-processing for wastewater treatment were summarized.

关键词： Chemical oxygen demand Mining-beneficiation wastewater treatment Particle swarm optimization Support vector regression Artificial neural network

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Particle swarm optimization model to predict scour depth around a bridge pier

Shahaboddin SHAMSHIRBAND, Amir MOSAVI, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年第14卷第4期页码 855-866 doi: 10.1007/s11709-020-0619-2

摘要： Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of 50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

关键词： scour depth bridge design and construction particle swarm optimization computational mechanics artificial intelligence bridge pier

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Ultrasound-guided prostate percutaneous intervention robot system and calibration by informative particleswarm optimization

《机械工程前沿（英文）》 2022年第17卷第1期页码 3-3 doi: 10.1007/s11465-021-0659-x

摘要： Applying a robot system in ultrasound-guided percutaneous intervention is an effective approach for prostate cancer diagnosis and treatment. The limited space for robot manipulation restricts structure volume and motion. In this paper, an 8-degree-of-freedom robot system is proposed for ultrasound probe manipulation, needle positioning, and needle insertion. A novel parallel structure is employed in the robot system for space saving, structural rigidity, and collision avoidance. The particle swarm optimization method based on informative value is proposed for kinematic parameter identification to calibrate the parallel structure accurately. The method identifies parameters in the modified kinematic model stepwise according to parameter discernibility. Verification experiments prove that the robot system can realize motions needed in targeting. By applying the calibration method, a reasonable, reliable forward kinematic model is built, and the average errors can be limited to 0.963 and 1.846 mm for insertion point and target point, respectively.

关键词： ultrasound image guidance prostate percutaneous intervention parallel robot kinematics identification particle swarm optimization informative value

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粒子群优化算法综述

杨维,李歧强

《中国工程科学》 2004年第6卷第5期页码 87-94

摘要：

粒子群优化（PSO）算法是一种新兴的优化技术，其思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。详细介绍了PSO的基本原理、各种改进技术及其应用等，并对其未来的研究提出了一些建议。

关键词：群体智能演化算法粒子群优化

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Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

Amin GHANNADIASL; Saeedeh GHAEMIFARD

《结构与土木工程前沿（英文）》 2022年第16卷第9期页码 1127-1140 doi: 10.1007/s11709-022-0838-9

摘要： The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure. Therefore, the accurate determination of crack characteristics, such as location and depth, is one of the key engineering issues for assessment of the reliability of structures. This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization (PSO); these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm (PSO-GA-FA), Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm (PSO-GWO-FA), and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO). A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters. Thus, the location and depth of a crack in a beam can be predicted by measuring its natural frequency. Hence, the measured natural frequency can be used as the input parameter of the algorithm. In this paper, this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms. The results show that among the proposed triple hybrid algorithms, the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.

关键词： crack cantilever beam triple hybrid algorithms Particle Swarm Optimization

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工程项目多目标协同优化研究

刘晓峰,陈通,吴绍艳

《中国工程科学》 2010年第12卷第3期页码 90-94

摘要：

将微粒群算法(particle swarm optimization，PSO)引入工程项目多目标协同优化领域，研究工程项目的质量、费用、资源和工期的协同优化问题

关键词：微粒群算法工程项目管理协同功效系数多目标协同规划模型算例

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基于改进粒子群算法优化的PID控制器在协同碰撞避免系统中的应用 Article

Xing-chen WU, Gui-he QIN, Ming-hui SUN, He YU, Qian-yi XU

《信息与电子工程前沿（英文）》 2017年第18卷第9期页码 1385-1395 doi: 10.1631/FITEE.1601427

摘要： cooperative collision avoidance system，CCAS）的研究中存在的不能合理优化PID控制器，以及对车辆行驶稳定性、舒适性及燃油经济性研究不足的问题，本文提出使用改进的粒子群优化算法（particleswarm optimization, PSO）优化PID控制器的方法，来实现CCAS对车辆更好的操控的目标。

关键词：协同碰撞避免系统；改进的粒子群算法；PID控制器；行驶舒适性；燃油经济性

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基于PSO优化LS-SVM算法的水电站厂房结构振动响应预测

练继建,何龙军,王海军

《中国工程科学》 2011年第13卷第12期页码 45-50

摘要：

依据二滩水电站地下厂房和机组的原型观测数据对机组和厂房结构振动的相关性进行分析，据此建立基于粒子群优化最小二乘支持向量计算法的厂房振动响应预测模型，预测结果与实测资料吻合。在此基础上将运行水头作为输入因子引入到智能预测模型中，扩大了该智能预测模型的适用范围，取得了很好的效果。

关键词：水电站厂房耦联振动粒子群优化算法最小二乘支持向量机响应预测

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Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 274-287 doi: 10.1007/s11705-021-2043-0

摘要： Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

关键词： smart chemical process operations data generation hybrid method machine learning particle swarm optimization