Abstract
Optimization plays a crucial role in economic load dispatch (ELD) due to the rising competition in the electricity market. In dealing with complex and challenging optimization problems, swarm intelligence has already demonstrated its skill. Hence, in this study, the optimization of non-smooth economic load dispatch (ELD) has been implemented by employing two popular metaheuristic algorithms, namely Artificial Bee Colony (ABC) and Genetic Algorithm (GA). However, before implementing a metaheuristic algorithm, it is crucial to understand how it performs in contrast to others. Owing to this, the comparative study between these two algorithms (ABC and GA) in terms of convergence characteristics, statistical performance, and computation time in ELD is outlined. The results reveal that ABC outperforms GA in the ELD problem when it comes to minimizing fuel costs, despite the fact that ABC’s computational time is slightly longer than that of GA.
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