基于多策略改进的蜜獾优化算法

Improved Honey Badger Optimization Algorithm Based on Multi-strategies

针对蜜獾算法(Honey Badger Algorithm,HBA)在解决复杂优化问题时存在收敛速度慢,容易陷入局部最优的问题,提出了一种基于多策略改进的蜜獾优化算法(IHBA).首先,引入Sobol序列初始化种群,增加种群的多样性;其次,引入动态自适应密度因子和黄金正弦策略,快速切割解空间,平衡全局搜索与局部搜索,提升寻优效率;然后,引入柯西变异策略,优化最优解,避免算法陷入局部最优.为验证算法的性能,选取7种智能算法与IHBA对比,对10个经典测试函数进行寻优对比和Wilcoxon秩和检验,仿真结果表明IHBA算法的收敛速度和寻优精度都得到了提升;最后,将IHBA用于求解2个实际的工程优化问题,仿真结果表明IHBA在解决工程优化问题时具有较好的鲁棒性和实用性.

Aiming at the problem that honey badger algorithm(HBA)has slow convergence speed and is easy to fall into local optimization when solving complex optimization problems,an improved honey badger algorithm(IHBA)based on multi-strategies is proposed.Firstly,Sobol sequence is introduced to initialize the initial population and increase the diversity of the population.Secondly,the dynamic adaptive density factor and golden sine strategy are introduced to cut the solution space quickly,which balance the global-local searching abilities and improve the optimization efficiency.Then,Cauchy mutation strategy is introduced to optimize the optimal solution to avoid the algorithm falling into local optimization.In order to verify the performance of the algorithm,seven intelligent algorithms are selected to compare with IHBA,and ten classical benchmark functions are used for optimization and Wilcoxon rank sum test.The simulation results show that the convergence speed and optimization accuracy of IHBA algorithm are improved.Finally,IHBA is applied to two practical engineering optimization problems.The simulation results show that IHBA has certain advantages in solving engineering optimization problems.