Teaching-learning-based optimization for different economic dispatch problems

This paper presents a teaching-learning-based algorithm (tlbo) to solve economic load dispatch (eld) problems involving different linear and non-linear constraints. the problem formulation also considers non-convex objective functions including the effect of valve-point loading and the multi-fuel option of large-scale thermal plants. many difficulties, such as multimodality, dimensionality and differentiability, are associated with the optimization of large scale non-linear constraint based non-convex economic load dispatch problems. tlbo is a population-based technique which implements a group of solutions to proceed to the optimum solution. tlbo uses two different phases; 'teacher phase' and 'learner phase', and uses the mean value of the population to update the solution. unlike other optimization techniques, tlbo does not require any parameter to be tuned, thus, making its implementation simpler. tlbo uses the best solution of the iteration to change the existing solution in the population, thereby increasing the convergence rate. in the present paper, teaching-learning-based optimization (tlbo) is applied to solve such types of complicated problems efficiently and effectively, in order to achieve a superior quality solution in a computationally efficient way. simulation results show that the proposed approach outperforms several existing optimization techniques. results also proved the robustness of the proposed methodology.