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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 metaheuristic

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

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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: the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle SwarmOptimization (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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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 lifethrough following the personal best solution of each particle and the global best value of the whole swarm

Keywords： swarm intelligence     evolutionary algorithm     particle swarm optimization    

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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 particle swarm

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： image guidance     prostate percutaneous intervention     parallel robot     kinematics identification     particle swarm    

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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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Dolphin swarm algorithm Article

Tian-qi WU,Min YAO,Jian-hua YANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 717-729 doi: 10.1631/FITEE.1500287

Abstract: By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfullyAt present, there are many well-implemented algorithms, such as particle swarm optimization, geneticinto optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ inswarm optimization, genetic algorithm, and artificial bee colony algorithm.The results show that in most cases, the dolphin swarm algorithm performs better.

Keywords： Swarm intelligence     Bio-inspired algorithm     Dolphin     Optimization    

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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 particle swarmFinally, 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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Optimal array factor radiation pattern synthesis for linear antenna array using cat swarm optimization Article

Gopi RAM, Durbadal MANDAL, Sakti Prasad GHOSHAL, Rajib KAR

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 570-577 doi: 10.1631/FITEE.1500371

Abstract: Cat swarm optimization (CSO) has been applied for the optimization of the control parameters of radiation

Keywords： Patch antenna     Linear antenna array     Cat swarm optimization (CSO)     Side lobe level (SLL)    

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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： artificial neural network     time-dependent loading     deep learning network     genetic algorithm     particle swarm    

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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 particle swarmSecondly, 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 survey of the pursuit–evasion problem in swarm intelligence Review

Zhenxin MU, Jie PAN, Ziye ZHOU, Junzhi YU, Lu CAO,junzhi.yu@ia.ac.cn,yujunzhi@pku.edu.cn,caolu_space2015@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1093-1116 doi: 10.1631/FITEE.2200590

Abstract: For complex functions to emerge in , it is important to understand the intrinsic mechanisms of biological s in nature. In this paper, we present a comprehensive survey of ;, which is a critical problem in biological groups. First, we review the problem of ; from three different perspectives: game theory, control theory and artificial intelligence, and bio-inspired perspectives. Then we provide an overview of the research on ; problems in biological systems and . We summarize predator pursuit behavior and prey behavior as predator–prey behavior. Next, we analyze the application of ; in from three perspectives, i.e., strong pursuer group vs. weak evader group, weak pursuer group vs. strong evader group, and equal-ability group. Finally, relevant prospects for future ; challenges are discussed. This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

Keywords： Swarm behavior     Pursuit–     evasion     Artificial systems     Biological model     Collective motion    

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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： neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable     Particle swarm    

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Title Author Date Type Operation

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

Shahaboddin SHAMSHIRBAND, Amir MOSAVI, Timon RABCZUK

Journal Article

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

Amin GHANNADIASL; Saeedeh GHAEMIFARD

Journal Article

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

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 particle swarm

Journal Article

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

Xuewei QI,Ke LI,Walter D. POTTER

Journal Article

Dolphin swarm algorithm

Tian-qi WU,Min YAO,Jian-hua YANG

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

Optimal array factor radiation pattern synthesis for linear antenna array using cat swarm optimization

Gopi RAM, Durbadal MANDAL, Sakti Prasad GHOSHAL, Rajib KAR

Journal Article

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

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 survey of the pursuit–evasion problem in swarm intelligence

Zhenxin MU, Jie PAN, Ziye ZHOU, Junzhi YU, Lu CAO,junzhi.yu@ia.ac.cn,yujunzhi@pku.edu.cn,caolu_space2015@163.com

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