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A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions.
- Source :
- Journal of King Saud University - Computer & Information Sciences; Sep2014, Vol. 26 Issue 3, p332-346, 15p
- Publication Year :
- 2014
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13191578
- Volume :
- 26
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Journal of King Saud University - Computer & Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 99404241
- Full Text :
- https://doi.org/10.1016/j.jksuci.2013.12.004