资源简介
实现了多目标遗传算法NSGA2,并带有详细注释及相关论文,读者只需根据具体问题简要修改,即可使用。实现了多目标遗传算法NSGA2,并带有详细注释及相关论文,读者只需根据具体问题简要修改,即可使用。
代码片段和文件信息
function f = evaluate_objective(x M V)
%% function f = evaluate_objective(x M V)
% Function to evaluate the objective functions for the given input vector
% x. x is an array of decision variables and f(1) f(2) etc are the
% objective functions. The algorithm always minimizes the objective
% function hence if you would like to maximize the function then multiply
% the function by negative one. M is the numebr of objective functions and
% V is the number of decision variables.
%
% This functions is basically written by the user who defines his/her own
% objective function. Make sure that the M and V matches your initial user
% input. Make sure that the
%
% An example objective function is given below. It has two six decision
% variables are two objective functions.
% f = [];
% %% objective function one
% % Decision variables are used to form the objective function.
% f(1) = 1 - exp(-4*x(1))*(sin(6*pi*x(1)))^6;
% sum = 0;
% for i = 2 : 6
% sum = sum + x(i)/4;
% end
% %% Intermediate function
% g_x = 1 + 9*(sum)^(0.25);
%
% %% objective function two
% f(2) = g_x*(1 - ((f(1))/(g_x))^2);
%% Kursawe proposed by Frank Kursawe.
% Take a look at the following reference
% A variant of evolution strategies for vector optimization.
% In H. P. Schwefel and R. M鋘ner editors Parallel Problem Solving from
% Nature. 1st Workshop PPSN I volume 496 of Lecture Notes in Computer
% Science pages 193-197 Berlin Germany oct 1991. Springer-Verlag.
%
% Number of objective is two while it can have arbirtarly many decision
% variables within the range -5 and 5. Common number of variables is 3.
f = [];
% objective function one
sum = 0;
for i = 1 : V - 1
sum = sum - 10*exp(-0.2*sqrt((x(i))^2 + (x(i + 1))^2));
end
% Decision variables are used to form the objective function.
f(1) = sum;
% objective function two
sum = 0;
for i = 1 : V
sum = sum + (abs(x(i))^0.8 + 5*(sin(x(i)))^3);
end
% Decision variables are used to form the objective function.
f(2) = sum;
%% Check for error
if length(f) ~= M
error(‘The number of decision variables does not match you previous input. Kindly check your objective function‘);
end
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 2216 2006-03-16 15:28 NSGA-II(带有详细注释及相关论文)\NSGA-II\evaluate_ob
文件 5695 2006-03-16 15:30 NSGA-II(带有详细注释及相关论文)\NSGA-II\genetic_operator.m
文件 2480 2008-04-11 16:59 NSGA-II(带有详细注释及相关论文)\NSGA-II\initialize_variables.m
文件 7654 2008-04-11 18:52 NSGA-II(带有详细注释及相关论文)\NSGA-II\non_domination_sort_mod.m
文件 134157 2006-03-19 19:24 NSGA-II(带有详细注释及相关论文)\NSGA-II\NSGA II(鼻祖).pdf
文件 8127 2006-03-16 15:29 NSGA-II(带有详细注释及相关论文)\NSGA-II\nsga_2.m
文件 2200 2006-03-19 19:12 NSGA-II(带有详细注释及相关论文)\NSGA-II\ob
文件 2719 2006-03-16 15:38 NSGA-II(带有详细注释及相关论文)\NSGA-II\replace_chromosome.m
文件 3627 2006-03-16 15:38 NSGA-II(带有详细注释及相关论文)\NSGA-II\tournament_selection.m
文件 137 2020-04-08 21:14 NSGA-II(带有详细注释及相关论文)\实现了多目标遗传算法NSGA2,并带有详细注释及相关论文,读者只需根据具体问题简要修改,即可使用。.txt
目录 0 2020-04-08 21:13 NSGA-II(带有详细注释及相关论文)\NSGA-II
目录 0 2020-04-08 21:13 NSGA-II(带有详细注释及相关论文)
----------- --------- ---------- ----- ----
169012 12
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