The particle swarm optimization (PSO) algorithm application – A review

Ovat, Friday Aje; Josephat, Anyandi Adie

doi:10.30574/gjeta.2020.3.3.0033

Cited by 24 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PSO is a global optimization algorithm based on swarm intelligence proposed by Kennedy et al in 1995 [27]. The PSO algorithm is a search algorithm that can be used for different complex problems [28][29][30][31][32][33]. It initializes a group of particles that do not have volume and mass first so that each particle is approximated as a feasible solution to the problem to be optimized.…”

Section: Pso Algorithmmentioning

confidence: 99%

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Zhao,

Bian

2024

Applied Sciences

View full text Add to dashboard Cite

In this paper, we aim to address the limited capacity of compensation devices by enhancing their utilization rate by applying the currents’ physical component (CPC) theory for reactive power optimization in three-phase four-wire systems. When reactive currents cannot be fully compensated for, we propose using CPC theory to generate reference currents for the compensation devices. Weight coefficients associated with different reactive current components are introduced, enabling flexible combinations of these independent current components. The maximum output amplitude of the three-phase current from the compensation device serves as a constraint condition, allowing for the calculation of reference currents under various compensation targets. Additionally, a reactive current optimization compensation scheme focusing on loss reduction is selected. The simulated annealing–particle swarm optimization (SA-PSO) hybrid algorithm is employed to solve the optimization mathematical model. The discussed calculations, current waveforms, and voltage waveforms are generated using the constructed mathematical model and then used for a theoretical explanation. The simulation verifies the feasibility of the proposed method.

show abstract

Section: Pso Algorithmmentioning

confidence: 99%

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Zhao,

Bian

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Liu et al [29] and Huang et al [30] employed road models to guide intelligent collectives in road anomaly detection and response. Ding et al [31] improved the Particle Swarm Optimization (PSO) [32] algorithm and applied the enhanced Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO) algorithm to solve task allocation problems, effectively addressing the reassignment of tasks in multi-agent emergency relief scenarios. Han et al [33] introduced an optimized A* path planning algorithm, allocating intelligent collectives and rapidly devising optimal flight paths.…”

Section: Multi-agent-based Collective Intelligence Responsementioning

confidence: 99%

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Wang,

Tian,

Pan

2023

Electronics

View full text Add to dashboard Cite

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates.

show abstract

“…The particle swarm algorithm arose from imitating birds foraging for food, and the food that the birds are looking for is the optimal solution to the problem [18]. Let the space of the problem be in H dimension and the solution of the population l is the particles of the population.…”

Section: Svm Algorithm Based On Pso and Adaptive Optimizationmentioning

confidence: 99%

Support Vector Machine based Automatic Classification Method for IoT big Data Features

Yong-Hua Xu

2023

Journal of Computers

View full text Add to dashboard Cite

<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.</p> <p> </p>

show abstract

The particle swarm optimization (PSO) algorithm application – A review

Cited by 24 publications

References 0 publications

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Support Vector Machine based Automatic Classification Method for IoT big Data Features

Contact Info

Product

Resources

About