混合多策略改进的斑点鬣狗优化算法及应用

Improved spotted hyena optimization algorithm based on hybrid multi-strategy and its applications

针对基本斑点鬣狗算法求解精度低、易陷入局部最优值等缺点,提出一种混合多策略改进的斑点鬣狗优化算法。通过Fuch混沌映射实现鬣狗种群的多样性,利用自适应惯性权重平衡斑点鬣狗算法的全局搜索和局部开发能力,引入拉普拉斯算子进行动态交叉操作,提高SHO算法跳出局部最优的能力,将小孔成像反向学习和逐维反向学习机制相结合,使种群更符合迭代规律,提高算法的收敛速度和寻优能力。通过若干基准函数的仿真实验进行比较,其结果表明,LPSHO具有更优越的寻优性能。将改进后LPSHO运用在机械优化设计和桁架结构优化工程问题上,与其它算法对比进一步表明LPSHO能够很好解决这类结构优化问题。

A hybrid multi-strategy improved spotted hyena optimization algorithm was proposed to address the shortcomings of the basic spotted hyena algorithm such as low solution accuracy and the tendency to fall into local optima.The diversity of the hyena population was achieved through Fuch chaotic mapping,followed by the use of adaptive inertia weights to balance the global search and local exploitation capabilities of the spotted hyena algorithm,and Laplace operators for dynamic crossover opera-tions were introduced to improve the ability of the SHO algorithm to jump out of the local optimum,the hole imaging inverse learning and dimension-by-dimension inverse learning mechanism were combined to make the population conform to the law of iteration,and the convergence speed and optimization ability of the algorithm were improved.The results were compared with a number of benchmark functions.Results demonstrate that LPSHO has better performance in finding the optimum.The improved LPSHO was applied to mechanical optimization design and truss structure optimization engineering,and compared with other algorithms.It shows that LPSHO can solve this kind of structure optimization problem well.