A Improved Archimedes Optimization Algorithm for multi/single-objective Optimal Power Flow

In this paper, an Improved Archimedes Optimization Algorithm (IAOA) is proposed to solve the Optimal Power Flow problem (OPF). The purpose of improving this IAOA algorithm is to increase population diversity in AOA, further improve the balance between the exploitation and exploration of AOA, and avoid premature convergence problems. The IAOA strategy uses a different approach to build a neighborhood for each object in which neighbor data can be transferred between objects. Dimension learning-based strategy is used for this process. The IAOA and AOA have been examined on the IEEE 30-bus, IEEE 57-bus and 16-bus South Marmara regional transmission systems. The effectiveness of the proposed IAOA and AOA are tested with the standard IEEE 30-bus and IEEE 57 -bus system and the simulation results are compared with different techniques as available published in the literature in recent years. In addition, in this study, an Offshore Wind Farm (OWF) and 16-bus South Marmara transmission system is modeled, and later OWF is integrated into a 16-bus South Marmara transmission system. Afterward, IAOA and other algorithms have tested for minimization of fuel emissions in this transmission system. The obtained simulation results and the comparison with different techniques show that the IAOA provides robustness.

Automated summary

An Improved Archimedes Optimization Algorithm (IAOA) is proposed in this paper to solve the Optimal Power Flow problem. By increasing population diversity, improving the balance between exploitation and exploration, and avoiding premature convergence, the IAOA algorithm is shown to be effective. The algorithm is tested and compared on different systems, demonstrating its robustness.