A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions
| Autor: | R. Venkata Rao, Gajanan Waghmare |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Continuous optimization
Mathematical optimization Meta-optimization General Computer Science Computer science Teaching–learning-based optimization Multi-objective optimization lcsh:QA75.5-76.95 Engineering optimization Vector optimization Test functions for optimization Unconstrained and constrained benchmark functions lcsh:Electronic computers. Computer science Multi-swarm optimization Metaheuristic |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 26, Iss 3, Pp 332-346 (2014) |
| ISSN: | 1319-1578 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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