nsga2算法matlab代码

以下是一个简单的NSGA-II算法的Matlab代码示例：

% NSGA-II算法示例
% 定义问题
problem.objective = @(x) [x(1)^2 + x(2)^2, (x(1)-1)^2 + x(2)^2];
problem.lb = [-5, -5]; % 变量下界
problem.ub = [5, 5]; % 变量上界

% 设置NSGA-II参数
params.PopulationSize = 100; % 种群大小
params.MaxGenerations = 50; % 最大迭代次数

% 运行NSGA-II算法
results = nsga2(problem, params);

% 输出结果
disp('最优解：');
disp(results.x);
disp('最优目标值：');
disp(results.obj);

请注意，上述代码中的`nsga2`函数并不存在于Matlab内置函数中。你需要自己实现或下载一个NSGA-II算法的函数库并将其添加到你的Matlab环境中。

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nsga2算法matlab代码实例

以下是NSGA-II算法的MATLAB代码示例：

function [pop, front] = nsga2(pop, nObj, nPop, nGen, crossRate, mutRate, bounds)

% NSGA-II Algorithm
% pop: population, front: non-dominated fronts
% nObj: number of objectives
% nPop: population size
% nGen: number of generations
% crossRate: crossover rate
% mutRate: mutation rate
% bounds: decision variable bounds

% Initialization
for i = 1:nPop
    pop(i).x = rand(1, nObj) .* (bounds(:,2)' - bounds(:,1)') + bounds(:,1)';
    pop(i).f = evaluate(pop(i).x);
    pop(i).rank = 0;
    pop(i).crowding = 0;
end

% Evolution
for gen = 1:nGen
    % Non-dominated sorting
    [pop, front] = non_dominated_sorting(pop);
    
    % Crowding distance assignment
    pop = crowding_distance_assignment(pop, front);
    
    % Offspring generation
    for i = 1:2:nPop
        % Select parents
        p1 = tournament_selection(pop, front);
        p2 = tournament_selection(pop, front);
        
        % Crossover
        if rand < crossRate
            [c1, c2] = simulated_binary_crossover(p1.x, p2.x);
        else
            c1 = p1.x;
            c2 = p2.x;
        end
        
        % Mutation
        if rand < mutRate
            c1 = polynomial_mutation(c1, bounds);
        end
        if rand < mutRate
            c2 = polynomial_mutation(c2, bounds);
        end
        
        % Create offspring
        pop(i+nPop).x = c1;
        pop(i+nPop).f = evaluate(c1);
        pop(i+nPop).rank = 0;
        pop(i+nPop).crowding = 0;
        
        pop(i+nPop+1).x = c2;
        pop(i+nPop+1).f = evaluate(c2);
        pop(i+nPop+1).rank = 0;
        pop(i+nPop+1).crowding = 0;
    end
    
    % Merge parent and offspring populations
    pop = [pop(1:nPop), pop(nPop+1:end)];
end

% Final non-dominated sorting
[pop, front] = non_dominated_sorting(pop);

end

function f = evaluate(x)

% Objective function
f(1) = x(1)^2 + x(2)^2;
f(2) = (x(1)-1)^2 + x(2)^2;

end

function [pop, front] = non_dominated_sorting(pop)

% Non-dominated sorting
nPop = length(pop);

% Initialize fronts and domination counts
front = {};
nDom = zeros(nPop, 1);

% Calculate domination counts
for i = 1:nPop
    for j = 1:nPop
        if i ~= j
            if dominates(pop(i), pop(j))
                nDom(i) = nDom(i) + 1;
            end
        end
    end
end

% Assign individuals to first front
front{1} = find(nDom == 0);

% Assign individuals to other fronts
iFront = 1;
while ~isempty(front{iFront})
    iFront = iFront + 1;
    for i = 1:length(front{iFront-1})
        p = front{iFront-1}(i);
        for j = 1:nPop
            if nDom(j) > 0
                nDom(j) = nDom(j) - 1;
                if nDom(j) == 0
                    front{iFront} = [front{iFront}, j];
                end
            end
        end
    end
end

% Assign ranks to population
for i = 1:length(front)
    for j = 1:length(front{i})
        pop(front{i}(j)).rank = i;
    end
end

end

function pop = crowding_distance_assignment(pop, front)

% Crowding distance assignment
nPop = length(pop);
nObj = length(pop(1).f);

for i = 1:length(front)
    nFront = length(front{i});
    
    if nFront > 2
        % Initialize crowding distance
        for j = 1:nFront
            pop(front{i}(j)).crowding = 0;
        end
        
        % Calculate crowding distance for each objective
        for m = 1:nObj
            % Sort individuals by objective value
            [~, order] = sort([pop(front{i}).f(m)]);
            
            % Assign infinite crowding distance to boundary individuals
            pop(front{i}(order(1))).crowding = Inf;
            pop(front{i}(order(end))).crowding = Inf;
            
            % Calculate crowding distance for non-boundary individuals
            for j = 2:nFront-1
                pop(front{i}(order(j))).crowding = ...
                    pop(front{i}(order(j))).crowding + ...
                    (pop(front{i}(order(j+1))).f(m) - pop(front{i}(order(j-1))).f(m)) / ...
                    (pop(front{i}(order(end))).f(m) - pop(front{i}(order(1))).f(m));
            end
        end
    end
end

end

function b = dominates(p1, p2)

% Dominance comparison
f1 = p1.f;
f2 = p2.f;

if all(f1 <= f2) && any(f1 < f2)
    b = true;
elseif all(f2 <= f1) && any(f2 < f1)
    b = false;
else
    b = false;
end

end

function p = tournament_selection(pop, front)

% Tournament selection
nPop = length(pop);
nFronts = length(front);

% Select random individuals
i1 = randi(nPop);
i2 = randi(nPop);

% Ensure that individuals are in different fronts
while pop(i1).rank == pop(i2).rank
    i2 = randi(nPop);
end

% Select individual with lower rank
if pop(i1).rank < pop(i2).rank
    p = pop(i1);
else
    p = pop(i2);
end

end

function [c1, c2] = simulated_binary_crossover(p1, p2)

% Simulated binary crossover
nVar = length(p1);
c1 = zeros(1, nVar);
c2 = zeros(1, nVar);

for i = 1:nVar
    if rand < 0.5
        if abs(p1(i) - p2(i)) > 1e-10
            if p1(i) < p2(i)
                y1 = p1(i);
                y2 = p2(i);
            else
                y1 = p2(i);
                y2 = p1(i);
            end
            beta = 1 + (2 * (y1 - bounds(i,1)) / (y2 - y1));
            alpha = 2 - beta^(-(1+1));
            u = rand;
            if u <= (1 / alpha)
                c1(i) = y1 + (y2 - y1) * (u * alpha)^(1/(1+1));
            else
                c1(i) = y1 + (y2 - y1) * (1/(2-u*alpha))^(1/(1+1));
            end
            u = rand;
            if u <= (1 / alpha)
                c2(i) = y1 + (y2 - y1) * (u * alpha)^(1/(1+1));
            else
                c2(i) = y1 + (y2 - y1) * (1/(2-u*alpha))^(1/(1+1));
            end
        else
            c1(i) = p1(i);
            c2(i) = p2(i);
        end
    else
        c1(i) = p1(i);
        c2(i) = p2(i);
    end
end

end

function c = polynomial_mutation(p, bounds)

% Polynomial mutation
nVar = length(p);
c = p;

for i = 1:nVar
    if rand < 1/nVar
        u = rand;
        if u <= 0.5
            delta = (2*u)^(1/(1+20)) - 1;
        else
            delta = 1 - (2*(1-u))^(1/(1+20));
        end
        c(i) = p(i) + delta * (bounds(i,2) - bounds(i,1));
    end
end

end

这是一个简单的双目标NSGA-II示例代码，其中包括选择、交叉、变异、非支配排序和拥挤距离分配。要使用此代码，您需要编写自己的目标函数，该函数将决策变量作为输入并返回目标函数值。您还需要指定一些算法参数，例如种群大小，交叉率和变异率。最后，您可以运行算法并获得一组非支配解以及它们所在的前沿。

nsga2算法matlab完整代码

要获取nsga2算法的完整MATLAB代码，您可以访问坎普尔遗传算法实验室（Campbell Genetic Algorithm Laboratory）的网站。他们是NSGA-II算法的开发者，并且在他们的网站上提供了更多相关信息和代码示例。<em>1</em><em>2</em><em>3</em>
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