High dimensional real parameter optimization with teaching learning based optimization

Abstract

Article history: Received 12 April 2012 Received in revised format 12 June 2012 Accepted June 22 2012 Available online 27 June 2012 In this paper, a new optimization technique known as Teaching–Learning-Based Optimization (TLBO) is implemented for solving high dimensional function optimization problems. Even though there are several other approaches to address this issue but the cost of computations are more in handling high dimensional problems. In this work we simulate TLBO for high dimensional benchmark function optimizations and compare its results with very widely used alternate techniques like Differential Evolution (DE) and Particle Swarm Optimization (PSO). Results clearly reveal that TLBO is able to address the computational cost issue for all simulated functions to a large dimensions compared to other two techniques. © 2012 Growing Science Ltd. All rights reserved