Abstract
Purpose: This research compares six optimization methods, including the Mayfly Optimization Algorithm, Genetic Algorithm, Simulated Annealing, Firefly Algorithm, and Differential Evolution (DE). Design/Methodology/Approach: The evaluation of any algorithm is predicated on its ability to strike a balance between meeting demand, taking into account renewable energy sources, and lowering the total cost of producing power. Findings: The analysis shows that although PSO and GA converge to similar overall costs, the algorithms' performances differ. Then come SA, FA, and DE in close succession, with the Mayfly Optimization Algorithm showing higher total costs than the other techniques. Originality: By addressing difficult power generating scheduling problems, this work advances our understanding of the benefits and drawbacks of various optimization techniques.
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