Effect of ant parameters on DEACO in economic load dispatch problem

A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years. This algorithm has turned into a candidate approach to many optimization problems. Unfortunately, this attractive algorithm suffers several drawbacks, including stagnation and slow convergence toward optimal solution. Thus, a new algorithm, termed as Differential Evolution Ant Colony Optimization (DEACO) has been modeled to compensate the drawbacks. The algorithm was utilized to solve the economic load dispatch problem in order to verify its performance. In this study, several DEACO parameters, including the number of ants and nodes were manipulated to investigate the behavior of the brand-new algorithm. Comparative studies between DEACO and conventional ACO suggested that the new algorithm had successfully overcome the weaknesses of classical ACO.