Firefly Optimization Algorithm for the Prediction of Uplift due to High-Pressure Jet Grouting

A project that needs to be uplifted by high-pressure jet grouting (HPJG) is exposed to particular geological and engineering circumstances; meanwhile, HPJG has intense subjectivity, short of the theoretical base, to ascertain the influence angle β and enlarged radius Δ a , which are the main parameters that affect the uplift effect. Therefore, we proposed a new method based on the firefly optimization algorithm to search for the optimal solution for the target function. Stochastic medium theory (SMT) was used in this article, in which the effect of single-pile HPJG was simulated as the superposition effect of the foam slurry at the same distance, to construct a stochastic medium prediction model of the effect of uplift due to multi-HPJG. In accordance with the range of the prediction results of single-pile HPJG and combined with in situ monitoring data to define the target function, the optimal parameters are substituted into the prediction model to predict the subsequent uplift effect due to HPJG. As a result of the global optimization capacity and by comparison with the genetic algorithm, the FOA has a greater advantage in terms of effectiveness and precision. Finally, it is proven that the prediction result meets the requirement of the prediction in advance by statistical data.

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Hybrid Stochastic Ranking and Opposite Differential Evolution-Based Enhanced Firefly Optimization Algorithm for Extending Network Lifetime Through Efficient Clustering in WSNs

Journal of Network and Systems Management ◽

10.1007/s10922-021-09597-6 ◽

2021 ◽

Vol 29 (3) ◽

Author(s):

A. Balamurugan ◽

M. Deva Priya ◽

Sengathir Janakiraman ◽

A. Christy Jeba Malar

Keyword(s):

Differential Evolution ◽

Network Lifetime ◽

Optimization Algorithm ◽

Firefly Optimization ◽

Firefly Optimization Algorithm

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An improved primary user emulation attack detection in cognitive radio networks based on firefly optimization algorithm

2016 33rd National Radio Science Conference (NRSC) ◽

10.1109/nrsc.2016.7450851 ◽

2016 ◽

Cited By ~ 11

Author(s):

Walid R. Ghanem ◽

Mona Shokair ◽

Moawad I. Desouky

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Optimization Algorithm ◽

Primary User ◽

Radio Networks ◽

Attack Detection ◽

Primary User Emulation Attack ◽

Primary User Emulation ◽

Firefly Optimization ◽

Firefly Optimization Algorithm

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Evaluation of the trust values among human resources in the enterprise cloud using an optimization algorithm and fuzzy logic

Kybernetes ◽

10.1108/k-04-2021-0280 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lantian Li ◽

Bahareh Pahlevanzadeh

Keyword(s):

Optimization Algorithm ◽

Heuristic Algorithms ◽

Human Life ◽

Cloud Services ◽

Cloud Provider ◽

Suggested Approach ◽

Content Type ◽

Firefly Optimization ◽

Security Costs ◽

Firefly Optimization Algorithm

PurposeCloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.Design/methodology/approachCloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.FindingsThe fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.Practical implicationsThe findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.Originality/valueUsing a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

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