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关键词

波粒二象性 3

粒子群优化 3

烟颗粒 2

粒子群优化算法 2

群体智能 2

BFT 1

Fe、Co、Ru 碳化物 1

Hilare 机器人 1

Pickering乳液 1

SPH 1

二维纳米颗粒 1

二维非线性动力学 1

代表性体积元 1

仿生算法；海豚；优化 1

优化调度 1

信息素 1

充电模式；充电时长；随机森林；长短期记忆网络（LSTM）；简化粒子群优化算法（SPSO） 1

光催化 1

光子 1

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Optimisation for interconnected energy hub system with combined ground source heat pump and borehole

Da HUO, Wei WEI, Simon Le BLOND

《能源前沿（英文）》 2018年第12卷第4期页码 529-539 doi: 10.1007/s11708-018-0580-0

摘要： Ground source heat pumps (GSHP) give zero-carbon emission heating at a residential level. However, as the heat is discharged, the temperature of the ground drops, leading to a poorer efficiency. Borehole inter-seasonal thermal storage coupled with GSHP maintains the efficiency at a high level. To adequately utilize the high performance of combined GSHP and the borehole system to further increase system efficiency and reduce cost, such a combined heating system is incorporated into the interconnected multi-carrier system to support the heat load of a community. The borehole finite element (FE) model and an equivalent borehole transfer function are proposed and respectively applied to the optimisation to analyze the variation of GSHP performance over the entire optimisation time horizon of 24 h. The results validate the borehole transfer function, and the optimisation computation time is reduced by 17 times compared with the optimisation using the FE model.

关键词： borehole thermal storage energy hub ground source heat pumps (GSHP) particle swarm optimisation

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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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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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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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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。该算法简单易实现，可调参数少，已得到广泛研究和应用。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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Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network

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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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

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背包问题的混合粒子群优化算法

高尚,杨静宇

《中国工程科学》 2006年第8卷第11期页码 94-98

摘要：

经典的粒子群是一个有效的寻找连续函数极值的方法，结合遗传算法的思想提出的混合粒子群算法来解决背包问题，经过比较测试，6种混合粒子群算法的效果都比较好，特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法，并成功地运用在投资问题中。对于目前还没有好的解法的组合优化问题，很容易地修改此算法就可解决。

关键词：粒子群算法背包问题遗传算法变异

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基于多目标粒子群协同算法的状态参数优化

丁雷,吴敏,佘锦华,段平

《中国工程科学》 2010年第12卷第2期页码 101-107

摘要：

针对铅锌烧结过程综合透气性、烧结终点的优化具有强非线性、计算复杂等特点，提出了一种有效的多目标粒子群协同优化算法。首先，建立了有综合透气性、烧结终点两个目标的优化模型。接着，通过改进的约束比较方法、粒子极值选取方法，以及利用不同的粒子群来分别优化相应的变量，提出了一种改进的多目标粒子群协同优化算法。最后，利用提出的多目标优化算法进行综合透气性、烧结终点的优化。仿真结果表明，所提出的多目标优化算法能较好地解决综合透气性、烧结终点的优化问题。

关键词：铅锌烧结过程综合透气性烧结终点多目标粒子群协同优化算法

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一种用于多功能相控阵雷达调度的混合遗传粒子群算法 Article

Hao-wei ZHANG, Jun-wei XIE, Wen-long LU, Chuan SHENG, Bin-feng ZONG

《信息与电子工程前沿（英文）》 2017年第18卷第11期页码 1806-1816 doi: 10.1631/FITEE.1601358

摘要：为解决多功能相控阵雷达的任务调度难题，本文提出了一种融合粒子群算法、遗传算法和启发式交错算法的优化算法。通过混沌理论优化粒子群算法的飞行参数，设计动态惯性权重，并引入遗传算法中的交叉、变异操作，使该算法的计算效率和寻优能力均得到大幅度提高。在智能算法的框架下，提出启发式的交错调度算法可进一步利用任务等待期的时间资源。仿真结果表明，与现有方法相比，本文算法效率更高，鲁棒性更强。

关键词：相控阵雷达；调度；粒子群算法；遗传算法；脉冲交错

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一种基于高斯过程与粒子群算法的CNN超参数自动搜索混合模型优化算法 Research Article

闫涵,仲崇权,吴玉虎,张立勇,卢伟

《信息与电子工程前沿（英文）》 2023年第24卷第11期页码 1557-1573 doi: 10.1631/FITEE.2200515

摘要：卷积神经网络（CNN）在许多实际应用领域中有着快速发展。然而，CNN性能很大程度上取决于其超参数，而为CNN配置合适的超参数通常面临着以下3个挑战：（1）不同类型CNN超参数的混合变量编码问题；（2）评估候选模型的昂贵计算成本问题；（3）确保搜索过程中收敛速率和模型性能问题。针对上述问题，提出一种基于高斯过程（GP）和粒子群优化算法（PSO）的混合模型优化算法（GPPSO），用于自动搜索最优的CNN超参数配置。首先，设计一种新的编码方法高效编码CNN中不同类型的超参数。其次，提出一种混合代理辅助（HSA）模型降低评估候选模型的高计算成本。最后，设计一种新的激活函数改善模型性能并确保收敛速率。在图像分类基准数据集上进行了大量实验，验证GPPSO优于最先进的方法。以金属断口诊断为例，验证GPPSO算法在实际应用中的有效性。实验结果表明，GPPSO仅需0.04和1.70 GPU天即可在CIFAR-10和CIFAR-100数据集上实现95.26％和76.36％识别准确率。

关键词：卷积神经网络；高斯过程；混合模型；超参数优化；混合变量；粒子群优化

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Data-driven approach to solve vertical drain under time-dependent loading

《结构与土木工程前沿（英文）》 2021年第15卷第3期页码 696-711 doi: 10.1007/s11709-021-0727-7

摘要： Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

关键词： vertical drain artificial neural network time-dependent loading deep learning network genetic algorithm particle swarm optimization

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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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Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG, Han WANG, Daojia CHEN

《机械工程前沿（英文）》 2019年第14卷第4期页码 422-433 doi: 10.1007/s11465-019-0552-z

摘要： Remanufacturing route optimization is crucial in remanufacturing production because it exerts a considerable impact on the eco-efficiency (i.e., the best link between economic and environmental benefits) of remanufacturing. Therefore, an optimization model for remanufacturing process routes oriented toward eco-efficiency is proposed. In this model, fault tree analysis is used to extract the characteristic factors of used products. The ICAM definition method is utilized to design alternative remanufacturing process routes for the used products. Afterward, an eco-efficiency objective function model is established, and simulated annealing (SA) particle swarm optimization (PSO) is applied to select the manufacturing process route with the best eco-efficiency. The proposed model is then applied to the remanufacturing of a used helical cylindrical gear, and optimization of the remanufacturing process route is realized by MATLAB programming. The proposed model’s feasibility is verified by comparing the model’s performance with that of standard SA and PSO.

关键词： remanufacturing process route optimization eco-efficiency simulated particle swarm optimization algorithm IDEF0