An Improved Teaching-Learning-Based Optimization Algorithm for Solving Economic Load Dispatch Problems

Abstract

Teaching-learning-based optimization algorithm (TLA) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. Aiming at the shortage of easily fall into local optimum when solving high dimensional complex optimization problems, in this paper, an improved teaching-learning-based optimization algorithm is proposed for economic load dispatch problems (ELD). The sub-population with reverse-learning strategy, the adaptive teaching factor and the differential evolution based Student phase were adopted to improve the basic TLA. Two test systems with thirteen and forty units are used to illustrate the effectiveness and accuracy of the proposed method. The results show that the proposed algorithm is a challenging method for ELD and is validated by comparing with the basic TLA.