Aida Fitria., Vivi 2024
The problem in the Economic Dispatch Problem (EDP) lies in optimizing the allocation of generation power to minimize operating expenses while meeting power system constraints. Various metaheuristic algorithms have been used to solve EDP, but many still face challenges in achieving a balance between exploration and exploitation. Orca Predation Algorithm (OPA) is a promising algorithm, but it still has some problems in its standard form. For example, it is sensitive to parameters, doesn’t offer a wide range of initial solutions, and may converge too soon, all of which can make it harder to find the best solutions for larger systems. To solve these issues, this study creates a modified OPA that uses three main methods: parameter tuning with grid search; uniform distribution initialization; and the use of self-adaptive momentum (SAMOPA). Grid search is used to find the best combination of parameters to improve the accuracy of the solution search. Uniform distribution is applied in the initialization stage to increase the initial diversity of the population and prevent local solution traps. Meanwhile, self-adaptive momentum is added to adaptively balance exploration and exploitation during algorithm iteration. The results indicate that the OPA modification significantly improves the performance in solving EDP. The modified algorithm is able to produce solutions with lower operation cost, faster convergence, and higher stability compared to other methods such as PSO, the bat algorithm, WOA, and GA. In addition, testing on various benchmark functions shows that the modification is not only effective in EDP but also has excellent robustness in other optimization problems. With these results, this research contributes to the development of more accurate, efficient, and applicable optimization methods for modern power systems.