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A New Sine Cosine Optimization Algorithm for Solving Combined Non-Convex Economic and Emission Power Dispatch Problems

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The current study presents a modified sine cosine optimization (MSCO) algorithm for solving the non-smooth environmental/economic power dispatch problem. In the proposed MSCO algorithm, random search agents’ population is initialized in the search domain for simultaneous optimization of both the combined economic and environmental objectives. Added to that, the proposed MSCO proposes an opposition strategy to preserve the diversity of solutions purposefully. Hence, the Pareto optimal solutions are customized according to the Pareto front concepts. These solutions are evolved using a modified version of the sine cosine algorithm (SCA), where the best agent is selected randomly from the stored Pareto solutions. Furthermore the parameter-based tuning mechanism is designed to improve the balance between the exploration and exploitation abilities. The correctness and effectiveness of the proposed MSCO are validated through experiments results and comparisons on EELD problem. Simulations were conducted on two test systems with non-smooth fuel cost and emission issues. The first system constitutes 6-unit benchmarking system, while the second one constitutes 10- units, and their results are compared with the results of other optimization techniques that were reported in the literature. The numerical comparisons reveal the robustness and effectiveness of the proposed MSCO algorithm.
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Environmental/Economic Dispatch Problem; Optimization; Pareto Optimal Solution; Sine Cosine Algorithm

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