Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem

Abstract

In this paper, a new optimization algorithm, namely Taguchi self-adaptive real-coded genetic algorithm (TSARGA) is proposed and implemented to solve economic dispatch (ED) problem with valve-point loading. The TSARGA combines the self-adaptive real-coded genetic algorithm with Taguchi method which can exploit the potential offspring. The self-adaptation is achieved by means of simulated binary crossover (SBX). Moreover, powerful exploration capability is achieved through tournament selection by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The systematic reasoning ability of the Taguchi method is incorporated after SBX operations to select the potential genes to achieve polynomial mutation, and consequently, enhance the robustness of the solution. The proposed TSARGA is effectively applied to solve the ED problem with valve-point loading with 6, 13 and 40-generator systems. The proposed method yields solutions towards global optimum and it compares far better with other methods in terms of solution quality, handling constraints and computation time.