Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system

In this paper, we present a multi-objective artificial bee colony (MOABC) algorithm and compare its efficiency with other existing algorithms for short-term scheduling of hydrothermal systems. We formulate the short-term combined economic and emission dispatch of hydrothermal systems as a complicated nonlinear optimization problem with a group of complex constraints. We modify the select operator of artificial bee colony algorithm to adapt the multi-objective problem optimization, and change the employed bee phase and probability calculation of onlooker bee phase to avoid local maxima. Furthermore, we utilize a progressive optimality algorithm based method to enhance the local search ability of the MOABC. Moreover, the constraint handling method has been proposed to resolve the complex constraints. We demonstrate the performance of the MOABC algorithm and compare it with other existing algorithms using the data from a hydrothermal power system in three different cases. The results show that the MOABC can obtain better schedule results with less fuel cost and environment pollution, more close to the true Pareto front and better diversification of non-dominated solutions compared to other existing methods.