An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems

By R. Venkata Rao, Vivek K. Patel. Scientia Iranica. International Journal of Science and Technology, 2012

404 citations recorded by the source corpus.

Abstract Teaching–Learning-Based Optimization (TLBO) algorithms simulate the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved TLBO algorithm are validated by comparing them with those obtained using the basic TLBO and other optimization algorithms available in the literature.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/4c5b5e93b34785bd07b6e93557ed74cbd9ed4fb3

How researchers see this paper

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In Artificial Intelligence

1. M. N. Ab Wahab
2. Samia Nefti-Meziani
3. Adham Atyabi

In Computational Theory and Mathematics

1. M. N. Ab Wahab
2. Samia Nefti-Meziani
3. Adham Atyabi

Citations in the corpus

• A Comprehensive Review of Swarm Optimization Algorithms (2015, background): “The fourth experiment is available in supplement section where the comparison between seven algorithms discussed on twenty benchmark functions with all results being collected from literature [140–157].”