Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review

Gad, Ahmed G.

doi:10.1007/s11831-021-09694-4

Cited by 652 publications

(220 citation statements)

References 206 publications

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“…The particle swarm optimization algorithm [ 15 , 16 , 17 ] is a population optimization algorithm derived from bird flock foraging, which focuses on guiding the optimization search through mutual cooperation and mutual search among flocks of birds [ 18 , 19 ]. The particle swarm algorithm is first initialized as a group of random particles that update themselves in iterations by tracking individual and global extremes [ 20 ].…”

Section: Csapso-bp Neural Network Algorithmmentioning

confidence: 99%

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Fang

Deng

et al. 2023

Sensors

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To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

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Section: Csapso-bp Neural Network Algorithmmentioning

confidence: 99%

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Fang

Deng

et al. 2023

Sensors

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“…Te strategic objectives, superior instructions, and troop opinions determine the feet's expected capability. To simplify the calculation, the feet's expected capabilities for diferent missions are set as the same, that is, (E (25,40,25,25,30). Te planner's risk preference determines the risk aversion coefcients α and β, which are 0.6 and 1.2, respectively.…”

Section: Problem Descriptionmentioning

confidence: 99%

Optimization of Fleet’s Level Repair Plan for Diverse Missions

Jiang

2023

Mathematical Problems in Engineering

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Various missions significantly impact the fleet’s level repair plan. The mission requirement model is established based on the “regular and dual control” repair mode. Considering the fleet’s overall operation, capability decay characteristics, and planner’s expectations and preferences, the fleet’s capability evaluation model is constructed. Furthermore, aiming at the maximum capability value and the minimum number of the mission ships and comprehensively considering constraints such as sailing rate, mission period, repair period, repair cost, capability restoration, and mission capability, the optimization model of the fleet’s level repair plan for diverse missions is established. The particle swarm optimization algorithm based on the hierarchical sequence method is used to solve the model, and the fleet’s level repair plan and mission configuration plan are obtained, which solves the coordination problem of the two types of plans. The results show that, compared with the traditional planning method, this method can fully consider the actual requirements of diverse missions and has better coordination of the relationship between the ship’s use and repair. This method can provide strong technical support for the scientific preparation of level repair plans and the effective completion of combat readiness training missions.

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“…Each swarm particle's position is updated such that it will move closer to the one with the best position. Each particle maintains pbest, the best solution each particle independently found, and gbest, the best solution found by all particles, to update its position and velocity in each iteration [26]. The processing steps of the PSO algorithm are mentioned below [27].…”

Section: Pso Algorithmmentioning

confidence: 99%

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

Korade¹,

Zuber²

2022

IJACSA

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Forecasting the correct stock price is intriguing and difficult for investors due to its irregular, inherent dynamics, and tricky nature. Convolutional neural networks (CNN) have impressive performance in forecasting stock prices. One of the most crucial tasks when training a CNN on a stock dataset is identifying the optimal hyperparameter that increases accuracy. In this research, we propose the use of the Firefly algorithm to optimize CNN hyperparameters. The hyperparameters for CNN were tuned with the help of Random Search (RS), Particle Swarm Optimization (PSO), and Firefly (FF) algorithms on different epochs, and CNN is trained on selected hyperparameters. Different evaluation metrics are calculated for training and testing datasets. The experimental finding demonstrates that the FF method finds the ideal parameter with a minimal number of fireflies and epochs. The objective function of the optimization technique is to reduce MSE. The PSO method delivers good results with increasing particle counts, while the FF method gives good results with fewer fireflies. In comparison with PSO, the MSE of the FF approach converges with increasing epoch.

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Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review

Cited by 652 publications

References 206 publications

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Optimization of Fleet’s Level Repair Plan for Diverse Missions

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

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