基于LHS和正余弦搜索的阿基米德优化算法

Archimedes Optimization Algorithm Based on LHS and Sine-cosine Search

针对阿基米德优化算法(AOA)寻优过程中存在兼顾全局探索和局部开发能力弱、寻优精度低、易陷入局部最优等问题,提出一种基于LHS和正余弦搜索算子的阿基米德优化算法(LSAOA)。首先,采用拉丁超立方抽样方法初始化种群,提高种群的均衡度和多样性;其次,改变全局搜索与局部搜索的切换模式,提高算法的收敛速度和精度;最后,引入正余弦搜索算子改进局部搜索方式,提高算法的局部搜索开发能力。仿真实验将LSAOA算法与其他改进AOA算法,以及其他元启发式算法在国际通用基准测试函数下进行寻优比较,实验结果表明,LSAOA算法在求解精度和收敛速度等方面具备较好的综合性能。

Aiming at the problems in the optimization process of Archimedes optimization algorithm(AOA),such as the weak ability of global exploration and local development,low optimization accuracy and easy to fall into local optimization,an Archi‐medes optimization algorithm based on LHS and sine-cosine search operator(LSAOA)is proposed.Firstly,Latin hypercube sampling method is used to initialize the population to improve the balance and diversity of the population;Secondly,the switch‐ing mode between global search and local search is changed to improve the convergence speed and accuracy of the algorithm;Fi‐nally,the sine-cosine search operator is introduced to improve the local search mode and improve the local search development ability of the algorithm.The simulation experiment compares lsaoa algorithm with other improved AOA algorithms and other meta heuristic algorithms under the international benchmark function.The experimental results show that lsaoa algorithm has better comprehensive performance in solving accuracy and convergence speed.