LARO: Opposition-Based Learning Boosted Artificial Rabbits-Inspired Optimization Algorithm with Lévy Flight

The artificial rabbits optimization (ARO) algorithm is a recently developed metaheuristic (MH) method motivated by the survival strategies of with bilateral symmetry in nature. Although ARO shows competitive performance compared popular MH algorithms, it still has poor convergence accuracy and problem getting stuck local solutions. In order to eliminate effects these deficiencies, this paper develops an enhanced variant ARO, called Lévy flight, selective opposition version rabbit (LARO) combining flight strategies. First, strategy introduced random hiding phase improve diversity dynamics population. diverse populations deepen global exploration process thus algorithm. Then, improved introducing enhance tracking efficiency prevent from current LARO various algorithms using 23 classical functions, IEEE CEC2017, CEC2019 functions. When faced three different test sets, was able perform best 15 (65%), 11 (39%), 6 (38%) respectively. practicality also emphasized addressing six mechanical problems. experimental results demonstrate that deals complicated problems through metrics.