A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions

• Main Authors: R. Venkata Rao, G.G. Waghmare
• Format: Article
• Language: English
• Published: Elsevier 2014-09-01
• Series: Journal of King Saud University: Computer and Information Sciences
• Subjects: Teaching–learning-based optimization; Multi-objective optimization; Unconstrained and constrained benchmark functions
• Online Access: http://www.sciencedirect.com/science/article/pii/S1319157813000967

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.