融合自适应权重与Levy飞行的拉丁超立方体海鸥优化算法及应用

A Latin hypercube seagull optimization algorithm and application combining adaptive weights and Levy flight

针对海鸥优化算法(Seagull optimization algorithm,SOA)收敛速度慢、寻优精度低以及搜索能力差等缺陷,提出一种融合自适应权重与Levy飞行的拉丁超立方体海鸥优化算法(Latin Hypercube Seagull Optimization Algorithm based onadaptive Weights and Levy flight,ALLSOA)。首先使用拉丁超立方体初始化海鸥种群,使海鸥种群全空间填充,分布更加均匀;其次在海鸥迁徙阶段,添加自适应权重因子,提高算法的搜索能力,加快算法收敛速度;最后在海鸥觅食阶段,采用Levy飞行策略,增加算法的多样性与跳出局部最优的能力,提高寻优精度。本文采用23个基准测试函数对改进算法进行测试,并利用图像分割来检验算法的有效性。试验结果表明,ALLSOA在收敛速度、寻优能力等方面表现更优。

Aiming at the shortcomings of the Seagull optimization algorithm(SOA),such as slow convergence speed,low optimization accuracy and poor search ability,this paper proposes a Latin Hypercube Seagull Optimization Algorithm based on adaptive weights and Levy flight(ALLSOA).Firstly,by using the Latin hypercube to initialize the seagull population,the whole space of the seagull population is filled and the distribution is more uniform;secondly,in the seagull migration stage,an adaptive weight factor is added to improve the global search ability of the algorithm and speed up the convergence of the algorithm;finally,in the seagull foraging stage,using the Levy flight strategy can increase the diversity of the algorithm and the ability to jump out of the local optimum,which also can improve the optimization accuracy.This paper uses 23 benchmark functions to test the improved algorithm,and applies the algorithm to image segmentation.The experimental results show that ALLSOA has better performance in terms of convergence speed and optimization ability.