A New Version of Artificial Rabbits Optimization for Solving Complex Bridge Network Optimization Problem

In recent years, meta-heuristic optimizers have been essential tools for solving real-world problems in many fields, such as engineering, science, and business. A newborn optimizer, so-called artificial rabbit optimization (ARO), is proposed based on survival strategies, detour foraging, and random hiding of rabbits in nature. Its performance was tested on 31 benchmark problems and five engineering design problems. It is observed that the ARO may face the problem of trapping at local optima and premature convergence. To remedy the aforementioned shortcomings of the ARO, a new version of ARO is developed based on a chaotic map (Chebyshev map) and Cauchy distribution random number-based mutation named CCARO in this paper. The refined ARO is tested on 13 benchmarks, and the findings are compared with that of the classical ARO and equilibrium optimizer (EO). The CCARO outperforms the competitors in most of the tasks. This demonstrates the ability of CCARO in solving complex optimization problems. In addition, one reliability redundancy allocation problem, the complex bridge network optimization problem (CBNOP), is solved using the CCARO to enhance the applicability range. It shows a superior performance that compares to the literature.