Orthogonal opposition-based learning honey badger algorithm with differential evolution for global optimization and engineering design problems

Abstract

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploration and exploitation. In this paper, an improved honey badger algorithm called ODEHBA is proposed to improve the performance of basic HBA. Firstly, an improved orthogonal opposition-based learning technique is employed to assist population in escaping local optimum. Secondly, differential evolution is utilized to ensure the enrichment of population diversity and to enhance convergence speed. Finally, the exploration capability of ODEHBA is boosted by an equilibrium pool strategy. To validate the efficacy of proposed ODEHBA, it is compared with 13 well-known metaheuristic algorithms on CEC2022 benchmark test sets. Friedman test and Wilcoxon rank-sum test are utilized to assess the performance of ODEHBA. Furthermore, three engineering design problems and Internet of Vehicles (IoV) routing problem are applied to validate the capability of ODEHBA. The simulation results demonstrate that ODEHBA excels in solving complex numerical problems, engineering design, and IoV routing problems. This holds significant practical implications for cost reduction and improved resource utilization.