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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

Frontiers in Energy 2018, Volume 12, Issue 4,   Pages 529-539 doi: 10.1007/s11708-018-0580-0

Abstract: FE) model and an equivalent borehole transfer function are proposed and respectively applied to the optimisationto 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 reducedby 17 times compared with the optimisation using the FE model.

Keywords： 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

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 9,   Pages 1127-1140 doi: 10.1007/s11709-022-0838-9

Abstract: deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on ParticleSwarm 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), andParticle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO).

Keywords： 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

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 4,   Pages 855-866 doi: 10.1007/s11709-020-0619-2

Abstract: Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristicMoreover, the ratio of pier width to flow depth and ratio of 50 (mean particle diameter) to flow depth

Keywords： scour depth     bridge design and construction     particle swarm optimization     computational mechanics     artificial    

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

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

Frontiers in Energy 2013, Volume 7, Issue 2,   Pages 174-181 doi: 10.1007/s11708-013-0246-x

Abstract: The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization

Keywords： 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

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1,   Pages 3-3 doi: 10.1007/s11465-021-0659-x

Abstract: The particle swarm optimization method based on informative value is proposed for kinematic parameter

Keywords： ultrasound image guidance     prostate percutaneous intervention     parallel robot     kinematics identification     particleswarm optimization     informative value    

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Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Strategic Study of CAE 2004, Volume 6, Issue 5,   Pages 87-94

Abstract:

Particle swarm optimization (PSO) is a new optimization technique originating from artificial lifeThe algorithm completes the optimization through following the personal best solution of each particleand the global best value of the whole swarm.

Keywords： swarm intelligence     evolutionary algorithm     particle swarm optimization    

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

Xuewei QI,Ke LI,Walter D. POTTER

Frontiers of Environmental Science & Engineering 2016, Volume 10, Issue 2,   Pages 341-351 doi: 10.1007/s11783-015-0776-z

Abstract: Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization

Keywords： particle swarm optimization (PSO)     diversity control     estimation of distribution algorithm (EDA)     water    

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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

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 274-287 doi: 10.1007/s11705-021-2043-0

Abstract: Thus, this paper presents an efficient hybrid framework of integrating machine learning and particleswarm optimization to overcome the aforementioned difficulties.Finally, optimal process operations were obtained by using the particle swarm optimization approach.

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

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Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 11,   Pages 94-98

Abstract:

The classical particle swarm optimization is a powerful method to find the minimum of a numericalThe particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommendedAll the 6 hybrid particle swarm optimization algorithms are proved effective.Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation

Keywords： particle swarm algorithm     knapsack problem     genetic algorithm     mutation    

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Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Strategic Study of CAE 2010, Volume 12, Issue 2,   Pages 101-107

Abstract: permeability and burn-through point in the lead-zinc sintering process, an efficient multi-objective particleswarm cooperative optimization algorithm is proposed.Secondly, an improved multi-objective particle swarm cooperative optimization algorithm is presented

Keywords： lead-zinc sintering process     synthetic permeability     burn-through point     multi-objective particle swarm    

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A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array Article

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

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1806-1816 doi: 10.1631/FITEE.1601358

Abstract: A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristicBy optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm

Keywords： Phased array radar     Scheduling     Particle swarm algorithm     Genetic algorithm     Pulse interleave    

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A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: s (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the and (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable     Particleswarm optimization    

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

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 696-711 doi: 10.1007/s11709-021-0727-7

Abstract: Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization

Keywords： drain     artificial neural network     time-dependent loading     deep learning network     genetic algorithm     particleswarm optimization    

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Using improved particle swarm optimization totune PID controllers in cooperative collision avoidance Article

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

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1385-1395 doi: 10.1631/FITEE.1601427

Abstract: Inthis paper, we propose a method to optimize PID controllers usingan improved particle swarm optimization

Keywords： Cooperative collision avoidance system (CCAS)     Improved particle swarm optimization (PSO)     PID controller    

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Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG, Han WANG, Daojia CHEN

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 4,   Pages 422-433 doi: 10.1007/s11465-019-0552-z

Abstract: Afterward, an eco-efficiency objective function model is established, and simulated annealing (SA) particleswarm optimization (PSO) is applied to select the manufacturing process route with the best eco-efficiency

Keywords： remanufacturing     process route optimization     eco-efficiency     simulated particle swarm optimization algorithm    

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Title Author Date Type Operation

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

Da HUO, Wei WEI, Simon Le BLOND

Journal Article

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

Amin GHANNADIASL; Saeedeh GHAEMIFARD

Journal Article

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

Shahaboddin SHAMSHIRBAND, Amir MOSAVI, Timon RABCZUK

Journal Article

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

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

Journal Article

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

Journal Article

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Journal Article

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

Xuewei QI,Ke LI,Walter D. POTTER

Journal Article

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

Journal Article

Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Journal Article

Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Journal Article

A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array

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

Journal Article

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

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

Journal Article

Using improved particle swarm optimization totune PID controllers in cooperative collision avoidance

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

Journal Article

Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG, Han WANG, Daojia CHEN

Journal Article