A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection

Chen, Zhen; Kun, Zhang; Chan, Tommy H.T.; Li, Xiaoke; Zhao, Shunbo

doi:10.3390/app12189036

Cited by 8 publications

(1 citation statement)

References 55 publications

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“…WOA has been widely applied across various domains, including parameter optimization [66], industrial design [67], power dispatch [68], and structural design [69]. Its ability to handle nonlinear, non-differentiable, continuous, and discrete optimization problems makes it an invaluable tool in areas requiring robust optimization solutions.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Olivares,

Noel,

Guzmán

et al. 2024

Biomimetics

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One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

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Section: Whale Optimization Algorithmmentioning

confidence: 99%

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Olivares,

Noel,

Guzmán

et al. 2024

Biomimetics

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Performance Analysis and Reliability Optimization of Internal Feedback Hydrostatic Rotary Table Support

Xie,

Yang,

et al. 2024

Journal of Tribology

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The internal feedback hydrostatic rotary table is a precision supporting device, the performance of which largely depends on the oil pad, a core component. However, uncertainties in the manufacturing process are often overlooked during the stiffness optimization, affecting the reliability of the optimized results. Accordingly, this paper aims to analyze the influence of structural parameters on the stiffness performance of the internal feedback hydrostatic rotary table and to perform reliability optimization considering the uncertainties. Initially, a theoretical computational model of internal feedback hydrostatic rotary table, accounting for the oil leakage effect, is proposed. The model's accuracy is validated through comparative simulation calculations, and based on this model, the load-bearing performance of the table is further analyzed. Subsequently, focusing on the structural characteristics of the oil pad, a reliability optimization model that considers manufacturing uncertainties is proposed. To improve the optimization efficiency, a Levenberg-Marquardt Backpropagation (LM-BP) neural network is introduced as a surrogate model for theoretical calculations. The oil pad is optimized through a particle swarm optimization algorithm. Ultimately, the optimal structural size parameters of the oil pad are obtained, achieving maximal stiffness under a high level of reliability. Both the stiffness performance and the reliability level of the rotary table are substantially enhanced. The results indicate that the proposed method significantly improves performance and reliability in practical applications.

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Photovoltaic MPPT control and improvement strategies considering environmental factors: based on PID-type sliding mode control and improved grey wolf optimization

Liu

2024

Measurement and Control

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Given the importance of promoting a greener and more sustainable future, it is crucial to promptly tackle and improve the issues surrounding carbon emissions and inefficiency linked to traditional energy sources. This study presents a new optimization method for PV systems. It combines an IGWO Algorithm with PID-type SMC to enhance the effectiveness of MPPT. Using IGWO, the optimal MPP voltage is determined even in the face of changing environmental conditions. Afterwards, the PID-type SMC adjusts the actual output voltage of the Boost based on the expected voltage to generate the required duty cycle. The integrated approach considers the natural fluctuations in PV systems, where changes in the environment can greatly affect the maximum power point. An in-depth evaluation was conducted using simulation software based on MATLAB, and a practical testing platform was built accordingly. The simulation and experimental results in real-world scenarios show that the new MPPT strategy has excellent overall performance and can quickly determine and track the voltage value for MPP compared to established algorithms. This study lays the groundwork for applying IGWO and new SMC control theories in the field of renewable energy generation. It also contributes to the development of MPPT technology, considering the challenges posed by the controlled environment.

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A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection

Cited by 8 publications

References 55 publications

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Performance Analysis and Reliability Optimization of Internal Feedback Hydrostatic Rotary Table Support

Photovoltaic MPPT control and improvement strategies considering environmental factors: based on PID-type sliding mode control and improved grey wolf optimization

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