Reproducing kernel approach for numerical solutions of fuzzy fractional initial value problems under the Mittag–Leffler kernel differential operator

Tran

Int J Comput Intell Syst

et al. 2022

The work in this paper presents a study into nature-inspired optimization applied to workload elasticity prediction using neural networks. Currently, the trend is for proactive decision support in increasing or decreasing the available resource in cloud computing. The aim is to avoid overprovision leading to resource waste and to avoid resource under-provisioning. The combination of optimization and neural networks has potential for the performance, accuracy, and stability of the prediction solution. In this context, we initially proposed an improved variant of sea lion optimization (ISLO) to boost the efficiency of the original in solving optimization problems. The designed optimization results are validated against eight well-known metaheuristic algorithms on 20 benchmark functions of CEC’2014 and CEC’2015. After that, improved sea lion optimization (ISLO) is used to train a hybrid neural network. Finally, the trained neural model is used for resource auto-scaling based on workload prediction with 4 real and public datasets. The experiments show that our neural network model provides improved results in comparison with other models, especially in comparison with neural networks trained using the original sea lion optimization. The proposed ISLO proved efficiency and improvement in solving problems ranging from global optimization with swarm intelligence to the prediction of workload elasticity.

Section: Related Workmentioning

confidence: 99%

An Improved Sea Lion Optimization for Workload Elasticity Prediction with Neural Networks

Tran

Int J Comput Intell Syst

et al. 2022

“…Senol et al [47] introduced a method, which is called the perturbationiteration algorithm, to find numerical solutions of some classes of fuzzy fractional partial differential equations in the sense of Caputo gH-differentiability. Abu Arqub et al [28,29] extended the reproducing kernel Hilbert space method in seeking the numerical solutions of FFDEs and fuzzy fractional Volterra and Fredholm integrodifferential equations in presence of the AtanganaBaleanuCaputo derivative. Ullah et al [30,31] used the fuzzy Laplace transform along with Adomian decomposition method to find the general numerical solutions for population dynamical model and SwiftHohenberg equations in the fuzzy environment.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Yang transform for second order fuzzy differential equations of integer and fractional order

Ullah

Ahmad

et al. 2023

Phys. Scr.

In this work, a Laplace-like transform in a fuzzy environment called Yang transform is introduced to solve fuzzy differential equations (FDEs) with the order θ ∈ (1, 2] involving the Caputo fractional derivative in the sense of gH-differentiability. Some basic properties of Yang transform for integer and fractional derivatives are also provided. Furthermore, by utilizing the combination between the Adomian decomposition method (ADM) and the Yang transform method, a general algorithm called the hybrid Yang transform method (HYTM) to solve the solutions of FDEs in the nonlinear form is proposed. For the validity and accuracy of this novel method, some examples and their simulations are given.

“…Notably, the fields of fuzzy differential equations and fuzzy dynamic systems have garnered significant attention due to their ability to effectively capture and model these uncertainties. The contributions made in the papers [6][7][8][9][10][11] have played a pivotal role in advancing our understanding and applications of these areas. Besides, the other recent approach is known as the set-theoretic approach, which has several names, including ellipsoidal analysis, worst-case scenario analysis, and interval analysis.…”

Section: Introductionmentioning

confidence: 99%

The solvability of interval-valued Abel integral equations on a time scale with trigonometric representation of parameterized interval analysis

Phut

Hoa

2023

Phys. Scr.