Application and performance comparison of variants of the firefly algorithm to the Economic Load Dispatch problem

Abstract

The Economic Load Dispatch problem is an optimization problem which minimizes cost such that the load demand is met and the generating equality and inequality constraints are satisfied. Previously, conventional techniques like linear programming and lambda iteration were applied to solve the economic dispatch problem given their simplicity. Nevertheless, they do not always converge to global optimum which gave rise to metaheuristic techniques such as evolutionary and bio-inspired swarm algorithms. Firefly algorithm is a swarm based recent metaheuristic that has a high convergence rate and short execution time compared to other metaheuristic techniques when solving the economic load dispatch problem. Given that the firefly algorithm has its shortcoming of getting trapped in local optima, many researchers have proposed modifications and hybrids that improve the performance of firefly algorithm to obtain optimum global solutions rapidly and efficiently. In this paper, three of these recent enhancements were adopted to solve the economic dispatch problem of six generating units. The performance of these variants was compared and analyzed. The results show high efficiency in achieving optimal results in less time than the original firefly algorithm.