Publication

Abstract : The Artificial rabbit optimization (ARO) algorithm replicates the survival skills of rabbits in the wild. However, like other metaheuristic approaches it possesses significant drawbacks in solving challenging problems, including sluggish convergence rate, poor exploration ability and trapped in local optima region. To alleviate these shortcomings, a novel strategy, namely Modified Random Opposition (MRO) and ten chaotic maps are integrated with ARO, termed as MROCARO. This implementation MRO technique boost the population diversity and permits the population to escape from local optima while integration of chaotic map enhances the exploitation capability. To estimate the effectiveness of the MROCARO method, the well-known CEC2005, CEC2017, CEC2019 and CEC2008lsgo test functions are considered. Moreover, non-parametric tests that include the Wilcoxon rank-sum and Friedman rank test are performed to analyze the significant difference among the compared algorithms. Furthermore, the efficiency of the MROCARO algorithm has been evaluated on various structural problems and optimal sizing of renewable energy systems. The experimental findings demonstrate that MROCARO performed optimum solution with 100% renewable sources with the lowest levelized cost of electricity of 0.0934 $/kWh as compared to other methods. Also, the simulation findings reveal that MROCARO has immense potential for addressing global optimization and structural problems as contrasted to other competing algorithms.