GDL

Journal from gdlhub / 2017-08-16 10:17:04
Oleh : R. Venkata Rao, G.G. Waghmare, King Saud University
Dibuat : 2014-09-16, dengan 1 file

Keyword : Teaching–learning-based optimization Multi-objective optimization Unconstrained and constrained benchmark functions
Url : http://www.sciencedirect.com/science/article/pii/S1319157813000967
Sumber pengambilan dokumen : web

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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