多目标遗传算法代码

.

% function nsga_2(pro)

%% Main Function

% Main program to run the NSGA-II MOEA.

% Read the corresponding documentation to learn more about multiobjective % optimization using evolutionary algorithms.

% initialize_variables has two arguments; First being the population size % and the second the problem number. '1' corresponds to MOP1 and '2' % corresponds to MOP2.

%inp_para_definition=input_parameters_definition;

%% Initialize the variables

% Declare the variables and initialize their values

% pop - population

% gen - generations

% pro - problem number

%clear;clc;tic;

pop = 100; % 每一代的种群数

gen = 100; % 总共的代数

pro = 2; % 问题选择1或者2，见switch

switch pro

case 1

% M is the number of objectives.

M = 2;

% V is the number of decision variables. In this case it is

% difficult to visualize the decision variables space while the % objective space is just two dimensional.

V = 6;

case 2

M = 3;

V = 12;

case 3 % case 1和case 2 用来对整个算法进行常规验证，作为调试之用；case 3

为本工程所需；

M = 2; %(output parameters 个数)

V = 8; %(input parameters 个数)

K = 10;

end

% Initialize the population

chromosome = initialize_variables(pop,pro);

%% Sort the initialized population

% Sort the population using non-domination-sort. This returns two columns % for each individual which are the rank and the crowding distance

% corresponding to their position in the front they belong. 真是牛 X了。chromosome = non_domination_sort_mod(chromosome,pro);

%% Start the evolution process

.

.

% The following are performed in each generation

% Select the parents

% Perfrom crossover and Mutation operator

% Perform Selection

for i = 1 : gen

% Select the parents

% Parents are selected for reproduction to generate offspring. The % original NSGA-II uses a binary tournament selection based on the % crowded-comparision operator. The arguments are

% pool - size of the mating pool. It is common to have this to be half the

% population size.

% tour - Tournament size. Original NSGA-II uses a binary tournament % selection, but to see the effect of tournament size this is kept

% arbitary, to be choosen by the user.

pool = round(pop/2);

tour = 2;

%下面进行二人锦标赛配对，新的群体规模是原来群体的一半

parent_chromosome = tournament_selection(chromosome,pool,tour);

% Perfrom crossover and Mutation operator

% The original NSGA-II algorithm uses Simulated Binary Crossover (SBX) and

% Polynomial crossover. Crossover probability pc = 0.9 and mutation % probability is pm = 1/n, where n is the number of decision variables. % Both real-coded GA and binary-coded GA are implemented in the original % algorithm, while in this program only the real-coded GA is considered. % The distribution indeices for crossover and mutation operators as mu = 20

% and mum = 20 respectively.

mu = 20;

mum = 20;

% 针对对象是上一步产生的新的个体parent_chromosome

%对parent_chromosome 每次操作以较大的概率进行交叉（产生两个新的候选人），或

者较小的概率变异（一个新的候选人）操作，这样

%就会产生较多的新个体

offspring_chromosome =

genetic_operator(parent_chromosome,pro,mu,mum);