GENERATOR FUEL COST OPTIMIZATION USING ANT COLONY ALGORITHM

Abstract

Ant Colony Algorithm (ACA) is an optimization algorithm was inspired by ant behavior when searching for the shortest distance from the food center. In this study, ACA is used for power plants with a fuel cost fitness function. ACA can search destinations faster than conventional methods such as Lagrange. In this study ACA used the optimal power flow of six power plants in the Java Bali 500 KV system, the optimization results reduced fuel costs by 23% and Lagrange 17.4% compared to real conditions.