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|3 10.33948/0584-026-003-008
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|a eng
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|b السعودية
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|9 524696
|a Rao, R. Venkata
|e Author
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245 |
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|a A Comparative Study Of A Teaching Learning Based Optimization Algorithm On Multi Objective Unconstrained And Constrained Functio
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260 |
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|b جامعة الملك سعود
|c 2014
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300 |
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|a 332 - 346
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|a بحوث ومقالات
|b Article
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520 |
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|b 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.
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653 |
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|a علوم الحاسوب
|a الخوارزميات
|a التصميم الهندسي
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692 |
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|b Teaching Learning Based Optimization
|b Multi Objective Optimization
|b Unconstrained And Constrained Benchmark Functions
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700 |
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|9 524698
|a Waghmare, G.G.
|e Co-Author
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773 |
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|c 008
|e Journal of King Saud University (Computer and Information Sciences)
|f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat
|l 003
|m مج26, ع3
|o 0584
|s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
|v 026
|x 1319-1578
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856 |
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|u 0584-026-003-008.pdf
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|d y
|p y
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|a science
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|c 973165
|d 973165
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