A Quantum Grey Wolf Optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework

Vijay, Rahul Kumar; Nanda, Satyasai Jagannath

doi:10.1016/j.jocs.2019.07.006

Cited by 32 publications

(6 citation statements)

References 24 publications

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“…In particular, the grey forecasting model is a predictive method that can establish mathematical models and make predictions through a small amount of incomplete information. In fact, when we make predictions in certain fields, such as weather forecasting (Li et al 2015), earthquake forecasting (Vijay and Nanda 2019), and pest forecasting (Wei 2016), the data provided are often a small amount, and sometimes it is not even possible to provide sufficient data so that modeling can be achieved. Therefore, it becomes crucial to identify a fairly proper model for small samples in practical applications.…”

Section: Research On the Grey Forecasting Modelmentioning

confidence: 99%

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Zhu

Xie

Shi

et al. 2021

Preprint

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Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

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Section: Research On the Grey Forecasting Modelmentioning

confidence: 99%

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Zhu

Xie

Shi

et al. 2021

Preprint

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“…Mehak Kohli [5] introduced chaos theory to the GWO algorithm, proposing the Chaos Grey Wolf Optimization algorithm (CGWO) for solving constrained optimization problems. Rahul Kumar Vijay [6] developed the Quantum ISSN 2616-5775 Vol. 7, Issue 3: 32-37, DOI: 10.25236/AJCIS.2024.070304…”

Section: Introductionmentioning

confidence: 99%

Modified Grey Wolf Optimization Hybridized with Teaching and Learning Mechanism for Solving Optimization Problems

2024

AJCIS

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The Grey Wolf Optimization (GWO) algorithm, inspired by grey wolf social behaviors, has shown excellent performance in various optimization problems. However, it faces limitations in handling dynamic optimization problems. To address this, we propose an enhanced version, Merged Teaching and Learning Grey Wolf Optimization (MTLGWO). MTLGWO introduces a two-phase teaching and learning strategy, improving global exploration and local exploitation capabilities. The core improvements include using Latin Hypercube Sampling for better population initialization and adopting a group teaching mechanism to simulate diverse teaching strategies. Through comprehensive performance testing on CEC2017 basic test functions, MTLGWO demonstrates superior performance in terms of convergence accuracy, stability, and convergence speed. Compared with other classical heuristic optimization algorithms, MTLGWO proves its potential and reliability as an efficient tool for solving optimization problems. These results highlight MTLGWO's potential as an efficient tool for practical optimization problems.

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“…Guo et al [14] proposed a simulated annealing and multi-objective discrete GWO with stochastic simulation-based approach. Vijay et al [15] proposed a Quantum Gray Wolf Optimizer to achieve the number of cluster events removed from the cube. Sun et al [16] proposed an optimal control strategy for a permanent magnet synchronous hub motor drive based on the GWO and control method.…”

Section: Introductionmentioning

confidence: 99%

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

Yuan

Qing

et al. 2023

Preprint

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Node localization has been one of the critical and significant research problems in wireless sensornetworks (WSN). The DV-Hop method determines the location of an unknown node based onthe anchor node location and the multi-hop forwarding principle, but the method falls far shortof satisfactory localization accuracy. Therefore, an improved Gray Wolf Optimizer (GWO) withcommunication strategy (IGWO-CS) is proposed in this paper. A convergence function based onthe sine function is proposed to eliminate the limitation of the convergence factor 𝑎 on the searchability of the GWO algorithm. A communication strategy based on Lévy flight is designed, which canimprove the efficiency of information interaction between wolves 𝛼, 𝛽 and 𝛿 while also improving thesearch accuracy and convergence speed. Extensive experimental results on 23 well-known benchmarkfunctions and unknown nodes location in WSN demonstrate the effectiveness of the proposed IGWO-CS method and also show sufficient competitiveness compared with other state-of-the-art algorithms,while the proposed IGWO-CS algorithm can achieve satisfactory performance in a short time.

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A Quantum Grey Wolf Optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework

Cited by 32 publications

References 24 publications

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Modified Grey Wolf Optimization Hybridized with Teaching and Learning Mechanism for Solving Optimization Problems

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

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