Novel hybrid of marine predator algorithm - Aquila optimizer for droop control in DC microgrid

This study presents a hybrid method, namely the marine predator algorithm (MPA) and Aquila optimizer (AO). The proposed algorithm is named MAO. AO duplicated the existence of the Aquila bird in nature while hunting for prey while MPA was inspired by predators in marine animal life. Although AO is widely accepted, it has several disadvantages. This causes various weaknesses such as a weak exploitation phase and slow growth of the convergence curve. Thus, certain exploitation and exploration in conventional AO can be studied to achieve the best balance. The MPA demonstrates the capacity to deliver optimal design and statistically efficient outcomes. The proposed method used AO as the main algorithm. To measure the performance of the proposed method, this study depicted a comparison using the AO, MPA, and whale optimization algorithm (WOA) methods. This paper was evaluated the performance of MAO on twenty-one CEC2017 benchmark functions test and droop control performance on direct current (DC) microgrid. From the simulation, MAO shows superior convergence ability. The proposed method and its application to droop control was successfully implemented and implied a promising performance.