Solving optimal power flow frameworks using modified artificial rabbit optimizer
Noor Habib Khan, Yong Wang, Raheela Jamal, Sheeraz Iqbal, Mohamed Ebeed, Muhammed Muneeb Khan, Yazeed Yasin Ghadi, Z. M. S. Elbarbary
Abstract
The present study introduces a nature inspired modified artificial rabbit optimizer (MARO) for solving the non-convex engineering optimization issues. The traditional artificial rabbit optimizer (t-ARO) reflects the survival strategies of the rabbits’ behaviors to avoid being hunted by the enemies, for which rabbits followed the detour scavenging and hiding strategies. However, the t-ARO still suffers from the stagnation complication and may cause of wrong in solution. To avoid early stagnation problem in t-ARO, the study proposes the three novel modifications in this approach. First modification is based on the fitness-distance balance (FDB) mechanism to boost up the searching capability of the rabbits’, while the second and third modifications are implemented to improve the exploitation strength of the t-ARO via prairie dogs (PD) and combination of quasi with opposite-based learning (QOBL) boosting mechanisms. To validate the effectiveness of the MARO, the statistical and non-parametric tests are conducted via standard benchmark functions. Furthermore, MARO is implemented to handle the single and the multiple objectives power flow frameworks using IEEE 30-bus and 57-bus standards. For authentication, the performance of proposed MARO is compared to the well-known techniques such as antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), dandelion optimizer (DO), artificial hummingbird algorithm (AHA), equilibrium optimizer (EO) and traditional artificial rabbit optimizer (t-ARO). The simulation outcomes declare that MARO establishes great superiority over the state-of-the-arts techniques.