基于正弦控制因子的Lateral变异鲨鱼优化算法

针对传统鲨鱼优化算法在求解高维目标函数时,易早熟收敛,陷入局部最优的缺陷.提出一种基于正弦控制因子的Lateral变异鲨鱼优化算法.通过正弦曲线的特性和自适应惯性权重,改善了传统鲨鱼优化算法中由于随机选取控制因子数值大小可能导致算法在迭代后期全局搜索能力降低的问题,提高了算法在迭代后期的全局收敛能力,并对最佳鲨鱼位置引入Lateral变异策略,加强了算法跳出局部最优的可能性.改进后的算法对多个shifted单峰,多峰以及固定维测试函数进行求解,实验结果表明,对比多种不同优化算法而言,本文所提LSSO算法具有更高的收敛精度和搜索速度.

Lateral Mutation Shark Smell Optimization Algorithm Based on Sinusoidal Control Factor

For the traditional shark optimization algorithm,when solving the high-dimensional objective function,it is easy to prematurely converge and fall into the local optimal defect.A Lateral variation shark optimization algorithm based on sinusoidal control factor is proposed.Through the characteristics of sinusoids and adaptive inertia weight,the problem of the global search ability of the algorithm in the late iterative period is improved due to the random selection of the control factor in the traditional shark optimization algorithm,and the global convergence ability of the algorithm in the later iterative period is improved.Introducing the Laterral mutation strategy for the optimal shark position enhances the possibility of the algorithm jumping out of local optimum.The improved algorithm solves multiple shifted single-peak,multi-peak and fixed-dimensional test functions.The experimental results show that compared with many different optimization algorithms,the proposed LSSO algorithm has higher convergence precision and search speed.