Smooth Exploration System: A novel ease-of-use and specialized module for improving exploration of whale optimization algorithm

Wu, Lei; Chen, Erqi; Guo, Qiang; Xu, Dengpan; Xiao, Wensheng; Guo, Jingjing; Zhang, Mowen

doi:10.1016/j.knosys.2023.110580

Cited by 6 publications

(4 citation statements)

References 115 publications

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“…The whale then starts its ascent, and the formation of bubbles continues unabated. As it approaches its prey, the target and bubble circle diminish, providing the hunter with an advantage to camouflage itself while simultaneously tracking and immobilizing its prey, ultimately catching it by surprise [84].…”

Section: 55mentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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Section: 55mentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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“…By training models on large datasets of experimental and simulated data, it is also possible to identify patterns and relationships that can inform the development of more accurate and predictive force fields 270 . Furthermore, ML and AI can also be used to accelerate simulations and optimize parameters, allowing for more efficient and accurate simulations of biological systems 271–275 . As computational power and data availability continue to increase, we can expect to see even more exciting developments in this area.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Application of computational approaches in biomembranes: From structure to function

Guo

Bao

et al. 2023

WIREs Comput Mol Sci

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Biological membranes (biomembranes) are one of the most complicated structures that allow life to exist. Investigating their structure, dynamics, and function is crucial for advancing our knowledge of cellular mechanisms and developing novel therapeutic strategies. However, experimental investigation of many biomembrane phenomena is challenging due to their compositional and structural complexity, as well as the inherently multi‐scalar features. Computational approaches, particularly molecular dynamics (MD) simulations, have emerged as powerful tools for addressing the atomic details of biomembrane systems, driving breakthroughs in our understanding of biomembranes and their roles in cellular function. This review presents an overview of the latest advancements in related computational approaches, from force fields and model construction to MD simulations and trajectory analysis. We also discussed current hot research topics and challenges. Finally, we outline future directions, emphasizing the integration of force field development, enhanced sampling techniques, and data‐driven approaches to accelerate the growth of this field in the years to come. We aim to equip readers with an understanding of the promise and limitations of emerging computational technologies in biomembrane systems and offer valuable recommendations for future research endeavors.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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“…Firstly, the optimal individual's position serves as a reference for identifying the best candidate set. Secondly, the remaining individuals progressively converge towards this identified best candidate set, while iterative position updates are conducted to iteratively approach the optimal solution [23]. The mathematical expression for this stage is presented below:…”

Section: Optimization Of the Woa Algorithmmentioning

confidence: 99%

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

Jiang,

Tang,

Jiang

et al. 2023

Buildings

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To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

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Smooth Exploration System: A novel ease-of-use and specialized module for improving exploration of whale optimization algorithm

Cited by 6 publications

References 115 publications

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Application of computational approaches in biomembranes: From structure to function

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

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