Bee-foraging learning particle swarm optimization

Xu, Chunxiao; Tianfield, Hugo; Du, Wenli

doi:10.1016/j.asoc.2021.107134

Cited by 55 publications

(21 citation statements)

References 57 publications

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“…Taking into account the onlooker bees mentioned in ABC [28], the particle with the best fitness values is sought in this onlooker stage; consequently, the best fitness value of each particle F bi is determined employing Eq. ( 5) and the probability of the i th particle P i being chosen can be computed according to Eq.…”

Section: ) Learning Of Onlookermentioning

confidence: 99%

A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

et al. 2023

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Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.INDEX TERMS Healthcare, BFL-PSO, sanitization, restoration, cloud storage, degree of modification, hiding ratio, and information preservation ratio.

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Section: ) Learning Of Onlookermentioning

confidence: 99%

A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

et al. 2023

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“…The evolutionary algorithm (EA) is an important branch of AI technology, and it is widely employed for solving complex optimization problems [24]. In recent years, many EAbased methodologies are widely developed and obtain promising performance in solving real-world optimization problems [25][26][27].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

Tang

et al. 2022

Sensors

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In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. The results of the case study show that the developed CCPSO-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.

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“…Compared with other optimization algorithms, the PSO algorithm is more prominent in optimization efficiency and stability [60]. Each particle represents a feasible solution of the problem to be optimized, and has two attributes of speed and position, which are expressed by Formulas ( 13) and ( 14), respectively [61][62][63]. In the iterative optimization process, the particle obtains the optimal solution satisfying the conditions by constantly updating the optimal positions of individuals and groups.…”

Section: Expert Decision Model Of Ipso Algorithmmentioning

confidence: 99%

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Zhang

Philbin

et al. 2021

Buildings

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With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.

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Bee-foraging learning particle swarm optimization

Cited by 55 publications

References 57 publications

A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

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