MGPA多目标优化算法附matlab代码+运行结果.zip

% Prepared by Jane J. Liang. Email: [email protected] February 20, 2005. benchmark_func.m is the main function for these minimization problems f=benchmark_func(x,func_num) x is the variable, f is the function value, func_num is the function num, data files save the necessary information. func_plot.m is used to plot the 2-D function map 25 functions in all, from 1 to 25, are Unimodal Functions (5): 1. Shifted Sphere Function Bounds[-100,100] f_bias=-450 2. Shifted Schwefel's Problem 1.2 Bounds[-100,100] f_bias=-450 3. Shifted Rotated High Conditioned Elliptic Function Bounds[-100,100] f_bias=-450 4. Shifted Schwefel's Problem 1.2 with Noise in Fitness Bounds[-100,100] f_bias=-450 5. Schwefel's Problem 2.6 with Global Optimum on Bounds Bounds[-100,100] f_bias=-310 Multimodal Functions (20): Basic Functions (7): 6. Shifted Rosenbrock's Function Bounds[-100,100] f_bias=390 7. Shifted Rotated Griewank's Function without Bounds Intilization Range [0, 600] f_bias=-180 8. Shifted Rotated Ackley's with Global Optimum on Bounds Bounds[-32,32] f_bias=-140 9. Shifted Rastrigin's Function Bounds[-5,5] f_bias=-330 10. Shifted Rotated Rastrigin's Function Bounds[-5,5] f_bias=-330 11. Shifted Rotated Weierstrass Function Bounds[-0.5,0.5] f_bias=90 12. Schwefel's Problem 2.13 Bounds[-100,100] f_bias=-460 Expanded Functions (2): 13. Expanded Extended Griewank's + Rosenbrock's (F8F2) Bounds[-3,1] f_bias=-130 14. Expanded Rotated Extended Scaffe's F6 Bounds[-100,100] f_bias=-300 Hybrid Composition Functions (11): 15. Hybrid Composition Function 1 Bounds[-5,5] f_bias= 120 16. Rotated Hybrid Comp. Fn 1 Bounds[-5,5] f_bias= 120 17. Rotated Hybrid Comp. Fn 1 with Noise in Fitness Bounds[-5,5] f_bias= 120 18. Rotated Hybrid Comp. Fn 2 Bounds[-5,5] f_bias=10 19. Rotated Hybrid Comp. Fn 2 with Narrow Global Optimal Basin Bounds[-5,5]] f_bias=10 20. Rotated Hybrid Comp. Fn 2 with the Global Optimum on Bounds Bounds[-5,5] f_bias=10 21. Rotated Hybrid Comp. Fn 3 Bounds[-5,5] f_bias=360 22. Rotated Hybrid Comp. Fn 3 with High Condition Number Matrix Bounds[-5,5] f_bias=360 23. Non-Continuous Rotated Hybrid Comp. Fn 3 Bounds[-5,5] f_bias=360 24. Rotated Hybrid Comp. Fn 4 Bounds[-5,5] f_bias=260 25. Rotated Hybrid Comp. Fn 4 without Bounds Intilization Range[-2,5] f_bias=260 ***Please note: When you use the test function, remember to set a global variable initial_flag, and make sure initial_flag=0 before each search. For details of the test functions, please read intro-2-functions.doc file %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~% Files: %~~~~~~~~~~~~~~~% Matlab *.m files: %~~~~~~~~~~~~~~~% benchmark_func.m %benchmark_func.m is the main function with all the minimization problems %f=benchmark_func(x,func_num) %x is the variable, f is the function value, func_num is the function number (1 to 25), func_plot.m %used to plot the 2-D function map %~~~~~~~~~~~~~~~% Matlab *.mat data files: %~~~~~~~~~~~~~~~% test_data.mat % 10 points (50D each) & corresponding fitnesses given to assist verification for code translation. % Variables:x1,x2,x3,....x25 % Corresponding Function Values: f1,f2,f3,....f25 ***Notice, for function 4,17,24,25, since they have noise, please set noise to 0 (e.g setting 0.0*N(0,1)) before test. fbias_data.mat % contain a 1*25 vector f_bias which are the global optimal function values. global_optima.mat % all 25 global optimal points (25 x 100 matrix) for the 25 test functions, % please note, function 5,8,20 set the global optima on the bounds, so the corresponding % global optima are: % if func_num==5,o(1:ceil(D/4))=-100;x(max(floor(0.75*D),1):D)=100;end % if func_num==8,o(2.*[1:floor(D/2)]-1)=-32;end % if func_num==20,o(1,2.*[1:floor(D/2)])=5;end sphere_func_data.mat schwefel_102_data.mat high_cond_elliptic_rot_data.mat elliptic_M_D2.mat elliptic_M_D10.mat elliptic_M_D30.mat elliptic_M_D50.mat schwefel_206_data.mat rosenbrock_func_data.mat griewank_func_data.mat griewank_M_D2.mat griewank_M_D10.mat griewank_M_D30.mat griewank_M_D50.mat ackley_func_data.mat ackley_M_D2.mat ackley_M_D10.mat ackley_M_D30.mat ackley_M_D50.mat rastrigin_func_data.mat rastrigin_M_D2.mat rastrigin_M_D10.mat rastrigin_M_D30.mat rastrigin_M_D50.mat weierstrass_data.mat weierstrass_M_D2.mat weierstrass_M_D10.mat weierstrass_M_D30.mat weierstrass_M_D50.mat schwefel_213_data.mat EF8F2_func_data.mat E_ScafferF6_func_data.mat E_ScafferF6_M_D2.mat E_ScafferF6_M_D10.mat E_ScafferF6_M_D30.mat E_ScafferF6_M_D50.mat hybrid_func1_data.mat hybrid_func1_M_D2.mat hybrid_func1_M_D10.mat hybrid_func1_M_D30.mat hybrid_func1_M_D50.mat hybrid_func2_data.mat hybrid_func2_M_D2.mat hybrid_func2_M_D10.mat hybrid_func2_M_D30.mat hybrid_func2_M_D50.mat hybrid_func3_data.mat hybrid_func3_M_D2.mat hybrid_func3_M_D10.mat hybrid_func3_M_D30.mat hybrid_func3_M_D50.mat hybrid_func4_data.mat hybrid_func4_M_D2.mat hybrid_func4_M_D10.mat hybrid_func4_M_D30.mat hybrid_func4_M_D50.mat %%%%%%%%%% PLEASE NOTE: hybrid_func1_M_D......matrix data in matlab mat format contain a structure variable M, and M.M1,M.M2...M.M10 are ten D*D matrix