A Comparative Study Of A Teaching Learning Based Optimization Algorithm On Multi Objective Unconstrained And Constrained Functio
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Rao, R. Venkata (Author) |
| مؤلفين آخرين: | Waghmare, G.G. (Co-Author) |
| المجلد/العدد: | مج26, ع3 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2014
|
| الصفحات: | 332 - 346 |
| DOI: |
10.33948/0584-026-003-008 |
| ISSN: |
1319-1578 |
| رقم MD: | 973165 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Teaching Learning Based Optimization | Multi Objective Optimization | Unconstrained And Constrained Benchmark Functions
|
| رابط المحتوى: |
| المستخلص: |
Multi-objective optimization is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Real-life engineering designs often contain more than one conflicting objective function, which requires a multi-objective approach. In a single-objective optimization problem, the optimal solution is clearly defined, while a set of trade-offs that gives rise to numerous solutions exists in multi-objective optimization problems. Each solution represents a particular performance trade-off between the objectives and can be considered optimal. In this paper, the performance of a recently developed teaching–learning-based optimization (TLBO) algorithm is evaluated against the other optimization algorithms over a set of multi-objective unconstrained and constrained test functions and the results are compared. The TLBO algorithm was observed to outperform the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. |
|---|---|
| ISSN: |
1319-1578 |
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