A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Beşkirli, Ayşe; Dağ, İdiris; Kiran, Mustafa Servet

doi:10.1016/j.asoc.2024.112220

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2024.112220

|View full text |Cite

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Ayşe Beşkirli,

İdiris Dağ,

Mustafa Servet Kiran

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization

Ishtaiwi,

Al-Shamayleh,

Fakhouri

2024

Applied Sciences

View full text Add to dashboard Cite

This paper presents the development and application of the JADESCA optimization algorithm for solving complex engineering design problems, including the welded beam, pressure vessel, spring, and speed reducer design problems. JADESCA, a hybrid algorithm that combines elements of JADE (differential evolution with adaptive parameters) and the sine cosine algorithm (SCA), is evaluated against a range of benchmark functions from the CEC2022 competition as well as specific engineering problems. The algorithm’s performance is analyzed through convergence curves, search history diagrams, and statistical results. In engineering design problems, JADESCA consistently demonstrates superior performance by achieving optimal or near-optimal solutions with high precision and consistency. In particular, JADESCA outperforms 25 state-of-the-art optimizers over the CEC2022 benchmark functions, further proving its robustness and adaptability. Statistical comparisons and Wilcoxon rank-sum tests reinforce the superiority of JADESCA in achieving competitive results across various test cases, solidifying its effectiveness in handling complex, constrained optimization problems for engineering applications.

show abstract

A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization

Ishtaiwi,

Al-Shamayleh,

Fakhouri

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators

Zhao,

Tao,

Jiang

et al. 2024

Biomimetics

View full text Add to dashboard Cite

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10−18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.

show abstract

Point Cloud Registration Method Based on Improved TLBO for Landing Gear Components Measurement

Xia,

Li,

et al. 2024

Symmetry

View full text Add to dashboard Cite

When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the components that make up the landing gear. However, when using traditional point cloud registration algorithms, if the initial pose between point clouds is poor, it can lead to significant errors in the final registration results or even registration failure. Furthermore, the significant difference in registration results between point clouds can affect the final measurement results. Adopting Teaching-Learning-Based Optimization (TLBO) to solve some optimization problems has unique advantages such as high accuracy and good stability. This study integrates TLBO with point cloud registration. To increase the probability of using TLBO for point cloud registration to search for the global optimal solution, adaptive learning weights are first introduced during the learner phase of the basic TLBO. Secondly, an additional tutoring phase has been designed based on the symmetry and unimodality of the normal distribution to improve the accuracy of the solution results. In order to evaluate the performance of the proposed algorithm, it was first used to solve the CEC2017 test function. The comparison results with other metaheuristics showed that the improved TLBO has excellent comprehensive performance. Then, registration experiments were conducted using the open point cloud dataset and the landing gear point cloud dataset, respectively. The registration results showed that the point cloud registration method proposed in this paper has strong competitiveness.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Cited by 3 publications

References 82 publications

A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization

A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization

Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators

Point Cloud Registration Method Based on Improved TLBO for Landing Gear Components Measurement

Contact Info

Product

Resources

About