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Journals / Journal of Multidisciplinary Modeling and Optimization / 2020 / Cilt: 3 - Sayı: 2

Bezier Curve Based Smoothing Penalty Function for Constrained Optimization

Ahmet SAHİNER
Nurullah YILMAZ
Gülden KAPUSUZ
Gamze ÖZKARDAŞ
Pages
70–79
Publication Date
2021-03-25
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, we apply our algorithm to test problems to demonstrate the effectiveness of the algorithm

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we consider nonlinear inequality constrained optimization problems.
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Key Findings & Data:We introduce l1 exact penalty function approach with a new smoothing function based on Bezier curve.
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Conclusion & Impact:Finally, we apply our algorithm to test problems to demonstrate the effectiveness of the algorithm
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:l1 penalty function · Smoothing · Non-smooth optimization

Abstract

In this study, we consider nonlinear inequality constrained optimization problems. We introduce l1 exact penalty function approach with a new smoothing function based on Bezier curve. Then, we propose a new algorithm by using the differentiation based methods to solve for solving l1 exact penalty functions. Finally, we apply our algorithm to test problems to demonstrate the effectiveness of the algorithm

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Cite This Article

SAHİNER, A., YILMAZ, N., KAPUSUZ, G., ÖZKARDAŞ, G. (2021). Bezier Curve Based Smoothing Penalty Function for Constrained Optimization. Journal of Multidisciplinary Modeling and Optimization, Cilt: 3 - Sayı: 2, 70–79. https://ajindex.com/articles/836264

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