Solving optimal power flow frameworks using modified artificial rabbit optimizer

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.