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.

A new hybrid model for photovoltaic output power prediction

“…With the advantage of simple mechanisms and few parameters, ARO has been employed to solve various optimization problems (Janamala et al 2022;Alsaiari et al 2023), nevertheless, the diversity often lacks in the initial population and the algorithm is easily trapped in local optima (Wang et al 2022b).…”

A hybrid quantum-classical approach to warm-starting optimization

“…15 In addition to the above mentioned versions of warm-start, referred to as the continuous warmstart procedure, Egger et al 5 have proposed modifications such as the rounded warm-start QAOA, where the initial state is generated by randomly rounding the SDP (semidefinite programming) relaxation of the QUBO problem. Another approach, called classically-boosted quantum optimization algorithm (CBQOA), 16 also uses a rounded solution of the SDP relaxation as initial state, followed by an efficiently-implementable continuous-time quantum walk.…”