Modified particle swarm optimization (MPSO) optimized CNN’s hyperparameters for classification by Murinto Murinto, Sri Winiarti

“…This paper proposes a convolutional neural network architectural design approach using the modified particle swarm optimization (MPSO) algorithm. Adjusting hyper-parameters and searching for optimal network architecture from convolutional neural networks (CNN) is an interesting challenge. …”
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Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization by Urbanus Mwanzia Ngei, Abraham Mutunga Nyete, Peter Musau Moses, Cyrus Wekesa

“…Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.…”
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On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems by M. Senthil Arumugam, M. V. C. Rao

“…Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. …”
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Multi-objective Optimal Design for Passive Part of Hybrid Active Power Filter Based on Bacterial Foraging and Particle Swarm Optimization by 李圣清, 李永安, 罗晓东, 曾黎琳, 何政平

“…With combination of particle swarm optimization algorithm and bacterial foraging optimization, an improved BFO-PSO optimized algorithm based on the random inertia factor and asynchronous time-dependent learning factor is used to solve optimal design,s problems of passive filter parameters for hybrid active filter. …”
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Multi-objective particle swarm algorithm based on angular segmentation archive and dynamic update tactics by Yi Luo, Yanmin Liu, Jianjie Chen, Jie Yang, Jiang Yu

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Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration by Zhiping Chen, Jia Zhang, Daren Zhang, Xiaolin Chang, Wei Zhou

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Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization by Liang Wang, Nan Sun

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An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project by Jinyan Shao, Yuan Lu, Yi Sun, Lei Zhao

“…Abstract In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. …”
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A Water Balance Model Based on Budyko Framework and Its Calibration through Particle Swarm Optimization Algorithm by Umut Okkan, Umut Kırdemir

“…Model was calibrated with particle swarm optimization (PSO) algorithm. To validate the performance of model calibrated via PSO, statistical measures were assessed. …”
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Dynamic adaptive discrete particle swarm optimization algorithm based method on low-power mapping in network-on-chip by Qin-rang LIU, Qi-hua DAI, Jian-liang SHEN, Bo ZHAO

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Self-tuning fuzzy PID control of manipulator grasping force based on improved particle swarm optimization algorithm by GUAN Shengqi, ZHANG Libo, LIU Tong, HAO Zhenhu

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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

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A Comparative Study of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Numerical Analysis of Fisher’s Equation by Geeta Arora, Kiran Bala, Homan Emadifar, Masoumeh Khademi

“…The aim of this research work is to obtain the numerical solution of Fisher’s equation using the radial basis function (RBF) with pseudospectral method (RBF-PS). The two optimization techniques, namely, particle swarm optimization (PSO) and artificial bee colony (ABC), have been compared for the numerical results in terms of errors, which are employed to find the shape parameter of the RBF. …”
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A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks by Long Cheng, Yan Wang, Shuai Li

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A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines by Yang Lu, Nianyin Zeng, Xiaohui Liu, Shujuan Yi

“…In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. …”
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A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm by Xiaoxiao Wei, Fan Tan, Peipei Yang, Hongchen Pan

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S-EPSO: A Socio-Emotional Particle Swarm Optimization Algorithm for Multimodal Search in Low-Dimensional Engineering Applications by Raynald Guilbault

“…The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. …”
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PID Control of Multisources Complex Excitations Active Vibration Isolation System: An Improved Particle Swarm Optimization Algorithm by Song Chunsheng, Jiang Youliang, Zhang Jinguang

“…Furthermore, this paper also sets up the acceleration feedback-based PID control model for multisources complex excitations active vibration isolation system, proposes an improved particle swarm optimization (PSO) algorithm of dynamic inertia weight factors used to optimize parameters of the built PID control model, and conducts simulation analysis. …”
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An Efficient Multisensor Hybrid Data Fusion Approach Based on Artificial Neural Networks and Particle Swarm Optimization Algorithms by Luc Eyembe Ihonock, Jean-François Dikoundou Essiben, Benjamin Salomon Diboma, Joe Suk Yong

“…The preprocessing stage of data is also included in the suggested technique, which starts with the design of the data-collecting device and ends with a hybrid model algorithm. Particle swarm optimization and artificial neural network methods are combined in the hybrid algorithm. …”
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Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm by Maohua Xiao, Weichen Wang, Kaixin Wang, Wei Zhang, Hengtong Zhang

“…First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. …”
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