Particle Swarm Optimization Algorithm Based on Factor Selection Strategy

Lv, Jiapeng; Shi, Xianjun

doi:10.1109/iaeac47372.2019.8997677

Cited by 13 publications

(2 citation statements)

References 1 publication

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“…The conventional particle swarm optimization process, according to their explanation, will prematurely stagnate as a result of the particle diversity limit and will thereafter reach a local optimum. In [ 15 ], authors suggested an optimization approach for particle swarms based on a particle swarm factor selection strategy (FPSO) to address the aforementioned drawbacks. Different parameter selection procedures are used at different phases of the particle swarm search process to enhance the capability of the global search.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Dissemination in VANETs Using Particle Swarm Optimization

Desai

El-Ocla

Purohit

2023

Sensors

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A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number of automobile connections in the car communications system. To ensure a high level of service and avoid unsafe situations brought on by congestion or a broadcast storm, data dissemination in a VANET network requires an effective approach. Effective multi-objective optimization methods are required to tackle this because of the implied competing nature of multi-metric approaches. A meta-heuristic technique with a high level of solution interactions can handle efficient optimization. To accomplish this, a meta-heuristic search algorithm particle optimization was chosen. In this paper, we have created a network consisting of vehicles as nodes. The aim is to send emergency messages immediately to the stationary nodes. The normal messages will be sent to the FIFO queue. To send these messages to a destination node, multiple routes were found using Time delay-based Multipath Routing (TMR) method, and to find the optimal and secure path Particle Swarm Optimization (PSO) is used. Our method is compared with different optimization methods such as Ant Colony Optimization (ACO), Firefly Optimization (FFO), and Enhanced Flying Ant Colony Optimization (EFACO). Significant improvements in terms of throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and the energy consumption are revealed by the experimental results.

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Section: Literature Reviewmentioning

confidence: 99%

Data Dissemination in VANETs Using Particle Swarm Optimization

Desai

El-Ocla

Purohit

2023

Sensors

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“…Particles keep the choices they tried by themselves and they have the knowledge which choices have outstand so far by their neighbours. This correlation provides the best choices in a swarm [9,10].…”

Section: Introductionmentioning

confidence: 99%

Analysing the shielding effectiveness of rectangular enclosure by determining aperture dimensions with particle swarm optimization

Güler¹

2022

Journal of Electrical Engineering

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Electromagnetic shielding enclosure is used to protect electronic circuits against external EMI. Aperture on the enclosure, which is necessary for various reasons such as mounting connector, ventilation attenuates shielding effectiveness (SE) of the enclosure. Enlarging enclosure dimensions makes SE get better. Yet, they canot be designed so large due to weight and dimension considerations for EV. When the dimensions of the shielding enclosure remain fixed and the aperture is to have a particular area, it is essential to optimize aperture dimensions to increase SE. In this paper, an optimization methodology based on PSO is designed to obtain the optimal SE for a particular dimension range. The study also provides a comparative analysis between designed optimization methodology and the one based on genetic algorithm in the literature. Obtained SE results indicate that the optimization methodology establishes a very good agreement with the results in the literature. Moreover, it has faster convergence and higher calculation accuracy than GA and it utilizes a smaller number of parameters thanks to its simplicity. Finally, it is concluded that through designed optimization methodology in this study, SE of the enclosure can be raised by optimizing aperture dimensions when the dimensions of shielding enclosure remain fixed.

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A Novel Spectral Ensemble Clustering Algorithm Based on Social Group Migratory Behavior and Emotional Preference

Dai

Feng

et al. 2022

Knowledge Science, Engineering and Management

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Particle Swarm Optimization Algorithm Based on Factor Selection Strategy

Cited by 13 publications

References 1 publication

Data Dissemination in VANETs Using Particle Swarm Optimization

Data Dissemination in VANETs Using Particle Swarm Optimization

Analysing the shielding effectiveness of rectangular enclosure by determining aperture dimensions with particle swarm optimization

A Novel Spectral Ensemble Clustering Algorithm Based on Social Group Migratory Behavior and Emotional Preference

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