Solving optimal power flow frameworks using modified artificial rabbit optimizer

摘要: 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 …