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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     artificialintelligence     bridge pier    

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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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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, geneticCombining these biological characteristics and living habits with swarm intelligence and bringing themswarm 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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A swarm intelligence design based on a workshop of meta-synthetic engineering Article

Bo-hu LI,Hui-yang QU,Ting-yu LIN,Bao-cun HOU,Xiang ZHAI,Guo-qiang SHI,Jun-hua ZHOU,Chao RUAN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1,   Pages 149-152 doi: 10.1631/FITEE.1700002

Abstract: In this paper, we present a swarm intelligence design technology based on a workshop of meta-syntheticengineering, including the architecture, the decision-making process of swarm intelligence design based

Keywords： Meta-synthetic engineering     Swarm intelligence     Design resources delivery    

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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: review the problem of ; from three different perspectives: game theory, control theory and artificial intelligence

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

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Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Strategic Study of CAE 2014, Volume 16, Issue 3,   Pages 108-112

Abstract:

The determinations of the rotation phase of quantum gates and mutation probability are the two main issues that restrict the efficiency of quantum genetic algorithm. This paper presents a quantum real coding genetic algorithm（QRGA）. QRGA used an adaptive means to adjust the direction and the size of the rotation angle of quantum rotation gate. In order to ensure the direction of evolution and population diversity，the mutation probability is guided based on the step of frog leaping algorithm which quantified by fuzzy logic. Comparative experimental results show that the algorithm can avoid falling into part optimal solution and astringe to the global optimum solution quickly，which has achieved good results in the running time and performance of the solution.

Keywords： quantum encoding     quantum genetic algorithm     frog leaping algorithm     swarm intelligence    

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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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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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An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application Article

Xiao-qing ZHANG, Zheng-feng MING

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1705-1719 doi: 10.1631/FITEE.1601555

Abstract: Due to its simplicity and ease of use, the standard grey wolf optimizer (GWO) is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism (MR-GWO). By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function (RBF) network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.

Keywords： Swarm intelligence     Grey wolf optimizer     Optimization     Radial basis function network    

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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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MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization Research Article

Kai MENG, Chen CHEN, Bin XIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1828-1847 doi: 10.1631/FITEE.2200237

Abstract: The (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal . Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an is designed to accommodate an adequate balance between exploration and exploitation. Finally, a is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering . The results demonstrate the superiority of the MSSSA in addressing practical problems.

Keywords： Swarm intelligence     Sparrow search algorithm     Adaptive parameter control strategy     Hybrid disturbance    

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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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INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Frontiers of Agricultural Science and Engineering 2023, Volume 10, Issue 4,   Pages 648-653 doi: 10.15302/J-FASE-2023505

Abstract: Therefore, this article proposes an interactive knowledge learning approach using artificial intelligence

Keywords： artificial intelligence     extension system     non-point source pollution control     smallholders     fertilization    

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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

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Journal Article

Dolphin swarm algorithm

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

Journal Article

A swarm intelligence design based on a workshop of meta-synthetic engineering

Bo-hu LI,Hui-yang QU,Ting-yu LIN,Bao-cun HOU,Xiang ZHAI,Guo-qiang SHI,Jun-hua ZHOU,Chao RUAN

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

Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Journal Article

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

Amin GHANNADIASL; Saeedeh GHAEMIFARD

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

An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application

Xiao-qing ZHANG, Zheng-feng MING

Journal Article

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

Xuewei QI,Ke LI,Walter D. POTTER

Journal Article

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG, Chen CHEN, Bin XIN

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

INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Journal Article