Improved Honey Badger Algorithm Based on Multi-Strategy and Its Applications

Abstract

The Honey Badger Algorithm(HBA) is a new type of intelligent optimization algorithm that simulates the foraging behavior of honey badgers. It has the characteristics of a simple structure and fast convergence speed. A multi-strategy improved Honey Badger algorithm(MSHBA) is proposed to address the issues of low convergence accuracy, slow convergence speed, and insufficient global optimization ability of the HBA to solve high-dimensional complex problems. It designs a restricted reverse learning mechanism that updates the population with restricted reverse solutions generated through algorithm iteration, improved population quality, and accelerated algorithm convergence speed. MSHBA introduces adaptive weight factors to adjust the optimization step size for different optimization paths as the number of iterations changes, thus coordinating different exploration stages of the algorithm, improving stability, and accelerating convergence speed; and construct a new hungry search strategy that changes the optimization step size for the optimization path based on population energy and global worst-case position to prevent premature convergence. Based on nine standard test functions, simulation experiments are conducted on the MSHBA, HBA, Whale Optimization, Harris Hawks, and single strategy in different dimensions. The results show that the MSHBA has better stability and convergence accuracy. The algorithm is applied to mechanical design optimization problems and the results are compared. Compared with the original HBA, the MSHBA achieved 88% performance optimization, confirming its suitability for solving high-dimensional complex problems.