Table of contents
2. Multiple intelligent optimization algorithms to solve CEC2022
2.1 This article participates in solving the intelligent optimization algorithm of CEC2022
2.2 Some test function operation results and convergence curves
3. Marked convergence curve code (you can change it yourself after obtaining the code)
1. CEC2022 test function
The CEC2022 test set has a total of 12 single-objective test functions, and the optional dimensions of each test function are 10 and 20 dimensions respectively. This test set is also the most widely used test set for high-quality papers. The CEC2022 test set has complex functions and is very challenging.
CEC2022 test function and theoretical optimal value
2. Multiple intelligent optimization algorithms to solve CEC2022
2.1 This article participates in solving the intelligent optimization algorithm of CEC2022
This article selects some classic intelligent optimization algorithms to participate in testing the CEC2022 test function, specifically: Sand Dune Cat Optimization Algorithm (SCSO), Dung Beetle Optimization Algorithm (DBO), Arithmetic Optimization Algorithm (AOA), Slime Mold Algorithm (SMA), Artificial Gorilla Force Optimization Algorithm (GTO), Snake Optimization Algorithm (SO), African Vulture Optimization Algorithm (AVOA) and Gray Wolf Algorithm (GWO), Whale Algorithm (WOA), Gravity Search Algorithm (GSA). The population size is set to 50 and the maximum number of iterations is 500.
2.2 Some test function operation results and convergence curves
F2:
The best optimal values of the objective funciton found by GSA is : 474.5299
The best optimal values of the objective funciton found by GWO is : 411.197
The best optimal values of the objective funciton found by WOA is : 416.4015
The best optimal values of the objective funciton found by AVOA is : 400.0163
The best optimal values of the objective funciton found by GTO is : 404.0752
The best optimal values of the objective funciton found by DBO is : 408.9161
The best optimal values of the objective funciton found by SO is : 407.3562
The best optimal values of the objective funciton found by SMA is : 407.083
The best optimal values of the objective funciton found by AOA is : 1154.5349
The best optimal values of the objective funciton found by SCSO is : 423.5253
F5:
The best optimal values of the objective funciton found by GSA is : 1006.2953
The best optimal values of the objective funciton found by GWO is : 900.1918
The best optimal values of the objective funciton found by WOA is : 3903.9517
The best optimal values of the objective funciton found by AVOA is : 953.213
The best optimal values of the objective funciton found by GTO is : 918.2926
The best optimal values of the objective funciton found by DBO is : 967.949
The best optimal values of the objective funciton found by SO is : 903.3099
The best optimal values of the objective funciton found by SMA is : 900.6152
The best optimal values of the objective funciton found by AOA is : 1378.7241
The best optimal values of the objective funciton found by SCSO is : 1437.0716
F8:
The best optimal values of the objective funciton found by GSA is : 2253.7266
The best optimal values of the objective funciton found by GWO is : 2225.0024
The best optimal values of the objective funciton found by WOA is : 2231.3405
The best optimal values of the objective funciton found by AVOA is : 2225.3089
The best optimal values of the objective funciton found by GTO is : 2204.8334
The best optimal values of the objective funciton found by DBO is : 2224.6929
The best optimal values of the objective funciton found by SO is : 2222.9351
The best optimal values of the objective funciton found by SMA is : 2220.9324
The best optimal values of the objective funciton found by AOA is : 2445.8039
The best optimal values of the objective funciton found by SCSO is : 2216.7517
F10：
The best optimal values of the objective funciton found by GSA is : 2636.9688
The best optimal values of the objective funciton found by GWO is : 2500.2949
The best optimal values of the objective funciton found by WOA is : 2500.6209
The best optimal values of the objective funciton found by AVOA is : 2500.4701
The best optimal values of the objective funciton found by GTO is : 2500.4813
The best optimal values of the objective funciton found by DBO is : 2500.5967
The best optimal values of the objective funciton found by SO is : 2500.351
The best optimal values of the objective funciton found by SMA is : 2500.5584
The best optimal values of the objective funciton found by AOA is : 2708.7742
The best optimal values of the objective funciton found by SCSO is : 2630.1491
F12:
The best optimal values of the objective funciton found by GSA is : 3026.5742
The best optimal values of the objective funciton found by GWO is : 2872.1718
The best optimal values of the objective funciton found by WOA is : 2911.8117
The best optimal values of the objective funciton found by AVOA is : 2865.403
The best optimal values of the objective funciton found by GTO is : 2863.4948
The best optimal values of the objective funciton found by DBO is : 2864.956
The best optimal values of the objective funciton found by SO is : 2865.3162
The best optimal values of the objective funciton found by SMA is : 2863.4012
The best optimal values of the objective funciton found by AOA is : 3035.8413
The best optimal values of the objective funciton found by SCSO is : 2868.6205
3. Marked convergence curve code (you can change it yourself after obtaining the code)
figure
maker_index=1:15:500;
semilogy(curve_compare(1,:),'k-*','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(2,:),'k-o','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(3,:),'b-<','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(4,:),'k-s','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(5,:),'k-d','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(6,:),'k-p','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(7,:),'r-p','MarkerIndices',maker_index,'LineWidth',1)
hold on
semilogy(curve_compare(8,:),'b-p','MarkerIndices',maker_index,'LineWidth',1)
xlabel('迭代次数');
ylabel('目标函数值');
grid on
box onIf you need the code, please send a private message to the blogger
