【优化求解】基于精子群优化算法结合引力搜索算法求解单目标问题matlab代码

1 简介This paper proposes a new hybrid optimization algorithm, called ‘‘(HSSOGSA)’’ with the combination of ‘‘gravitationalsearch algorithm (GSA)’’ and ‘‘sperm swarm optimization (SSO)’’. The underlying concepts and ideas behind the proposedalgorithm are to combine the capability of exploitation in SSO with the capability of exploration in GSA to synthesize bothalgorithms’ strength. To evaluate the effificiency of the proposed approach, different test bed problems of optimization areconsidered, called the ‘‘congress on evolutionary computation (CEC)’’ 2017 suite. The proposed HSSOGSA is comparedagainst both the standard GSA and SSO algorithms. These algorithms are compared based on two mechanisms, including,qualitative and quantitative tests. For the quantitative test, we adopt best fifitness, standard deviation, and average measures,while for the qualitative test, we compare between the convergence rates achieved by the proposed algorithm and theconvergence rates achieved by SSO and GSA. The outcomes of the study present the hybrid method possesses a bettercapability and performance to escape from local extremes with faster rate of convergence than the standard SSO and GSAfor the majority of benchmarks functions of wide and narrow search space domain.2 部分代码%HSSOGSA source code, Generated by Hisham A. Shehadeh, 2021. %Adopted from: Hisham A. Shehadeh, 鉄hehadeh, H. A. (2021). A hybrid sperm swarm % This function gives boundaries and dimension of search space for test functions. function [down,up,dim]benchmark_functions_details(Benchmark_Function_ID) %If lower bounds of dimensions are the same, then down is a value. %Otherwise, down is a vector that shows the lower bound of each dimension. %This is also true for upper bounds of dimensions. %Insert your own boundaries with a new Benchmark_Function_ID. dim30; if Benchmark_Function_ID1 down-100;up100; end if Benchmark_Function_ID2 down-10;up10; end if Benchmark_Function_ID3 down-100;up100; end if Benchmark_Function_ID4 down-100;up100; end if Benchmark_Function_ID5 down-30;up30; end if Benchmark_Function_ID6 down-100;up100; end if Benchmark_Function_ID7 down-1.28;up1.28; end if Benchmark_Function_ID8 down-500;up500; end if Benchmark_Function_ID9 down-5.12;up5.12; end if Benchmark_Function_ID10 down-32;up32; end if Benchmark_Function_ID11 down-600;up600; end if Benchmark_Function_ID12 down-50;up50; end if Benchmark_Function_ID13 down-50;up50; end if Benchmark_Function_ID14 down-65.536;up65.536;dim2; end if Benchmark_Function_ID15 down-5;up5;dim4; end if Benchmark_Function_ID16 down-5;up5;dim2; end if Benchmark_Function_ID17 down[-5;0];up[10;15];dim2; end if Benchmark_Function_ID18 down-2;up2;dim2; end if Benchmark_Function_ID19 down0;up1;dim3; end if Benchmark_Function_ID20 down0;up1;dim6; end if Benchmark_Function_ID21 down0;up10;dim4; end if Benchmark_Function_ID22 down0;up10;dim4; end if Benchmark_Function_ID23 down0;up10;dim4; end3 仿真结果​4 参考文献Mirjalili S, Lewis A (2016) The whale optimization algorithm.Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.0082. Fausto F, Reyna-Orta A, Cuevas E, Andrade A´ G, Perez-CisnerosM (2020) From ants to whales: metaheuristics for all tastes. ArtifIntell Rev 53(1):753–810. https://doi.org/10.1016/j.advengsoft.2016.01.008博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真有科研问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。