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

Abstract

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

Keywords

• artificial rabbits optimization
• Lévy flight
• selective opposition
• benchmark
• engineering optimization