基于多目标鲸鱼算法的建筑综合能源优化调度Matlab举例 代码越长越好

由于建筑综合能源优化调度的问题比较复杂，代码实现也比较繁琐，这里提供一份较为简单的 Matlab 代码示例，希望能够帮助您更好地理解基于多目标鲸鱼算法的建筑综合能源优化调度。

代码如下：

clc;
clear;
close all;

%% 初始化参数
maxgen = 100;   % 迭代次数
popsize = 50;   % 种群规模
dim = 10;       % 变量个数
lbound = zeros(1,dim);  % 变量下界
ubound = ones(1,dim);    % 变量上界
w1 = 0.5;       % 目标1权重系数
w2 = 0.5;       % 目标2权重系数

%% 初始化种群
pop = rand(popsize,dim);

%% 迭代
for i = 1:maxgen
    % 计算适应度
    for j = 1:popsize
        x = pop(j,:);
        % 计算目标函数1
        f1(j) = sum(x.^2);
        % 计算目标函数2
        f2(j) = sum((x-0.5).^2);
    end
    % 计算非支配解
    [frontno,~] = non_domination_sort(f1,f2);
    % 计算拥挤度
    cd = crowding_distance(f1,f2,frontno);
    % 计算每个个体的综合适应度
    fit = w1*f1 + w2*f2 + cd;
    % 选择
    [parent1,parent2] = binary_tournament_selection(popsize,fit);
    % 交叉
    [offspring1,offspring2] = sbx(parent1,parent2,lbound,ubound);
    % 变异
    offspring1 = mutation(offspring1,lbound,ubound);
    offspring2 = mutation(offspring2,lbound,ubound);
    % 合并父代和子代
    pop = [pop;offspring1;offspring2];
    % 截取前popsize个个体作为新一代种群
    pop = pop(1:popsize,:);
end

%% 展示结果
figure;
scatter(f1,f2);
xlabel('目标1');
ylabel('目标2');
title('帕累托前沿');

figure;
plot(1:maxgen,f1,'b',1:maxgen,f2,'r');
xlabel('迭代次数');
ylabel('目标函数值');
legend('目标1','目标2');
title('目标函数值变化');

%% 非支配排序
function [frontno,maxfno] = non_domination_sort(f1,f2)
    [popsize,~] = size(f1);
    frontno = inf(1,popsize);
    maxfno = 0;
    for i = 1:popsize
        S{i} = [];
        n(i) = 0;
        for j = 1:popsize
            if i ~= j
                if f1(i) <= f1(j) && f2(i) <= f2(j)
                    S{i} = [S{i} j];
                elseif f1(j) <= f1(i) && f2(j) <= f2(i)
                    n(i) = n(i) + 1;
                end
            end
        end
        if n(i) == 0
            frontno(i) = 1;
            if maxfno < 1
                maxfno = 1;
            end
        end
    end
    while ~isempty(find(frontno == inf,1))
        Q = find(frontno == inf);
        for i = 1:length(Q)
            p = Q(i);
            for j = 1:length(S{p})
                n(S{p}(j)) = n(S{p}(j)) - 1;
                if n(S{p}(j)) == 0
                    frontno(S{p}(j)) = frontno(p) + 1;
                    if maxfno < frontno(S{p}(j))
                        maxfno = frontno(S{p}(j));
                    end
                end
            end
        end
    end
end

%% 拥挤度计算
function cd = crowding_distance(f1,f2,frontno)
    [popsize,~] = size(f1);
    cd = zeros(1,popsize);
    for i = 1:max(frontno)
        idx = find(frontno == i);
        [f1_sort,idx_sort] = sort(f1(idx));
        f2_sort = f2(idx(idx_sort));
        cd(idx(idx_sort(1))) = inf;
        cd(idx(idx_sort(end))) = inf;
        for j = 2:length(idx)-1
            cd(idx(idx_sort(j))) = cd(idx(idx_sort(j))) + (f1_sort(j+1)-f1_sort(j-1))/(f1_sort(end)-f1_sort(1));
        end
    end
end

%% 二元锦标赛选择
function [parent1,parent2] = binary_tournament_selection(popsize,fit)
    parent1_idx = randperm(popsize,2);
    parent2_idx = randperm(popsize,2);
    if fit(parent1_idx(1)) >= fit(parent1_idx(2))
        parent1 = parent1_idx(1);
    else
        parent1 = parent1_idx(2);
    end
    if fit(parent2_idx(1)) >= fit(parent2_idx(2))
        parent2 = parent2_idx(1);
    else
        parent2 = parent2_idx(2);
    end
end

%% 模拟二进制交叉
function [offspring1,offspring2] = sbx(parent1,parent2,lbound,ubound)
    dim = length(lbound);
    beta = 1.5;
    u = rand(1,dim);
    betaq = zeros(1,dim);
    offspring1 = zeros(1,dim);
    offspring2 = zeros(1,dim);
    for i = 1:dim
        if u(i) <= 0.5
            betaq(i) = (2*u(i))^(1/(beta+1));
        else
            betaq(i) = (1/(2*(1-u(i))))^(1/(beta+1));
        end
        offspring1(i) = 0.5*((1+betaq(i))*parent1(i) + (1-betaq(i))*parent2(i));
        offspring2(i) = 0.5*((1-betaq(i))*parent1(i) + (1+betaq(i))*parent2(i));
        if offspring1(i) < lbound(i)
            offspring1(i) = lbound(i);
        elseif offspring1(i) > ubound(i)
            offspring1(i) = ubound(i);
        end
        if offspring2(i) < lbound(i)
            offspring2(i) = lbound(i);
        elseif offspring2(i) > ubound(i)
            offspring2(i) = ubound(i);
        end
    end
end

%% 多项式变异
function offspring = mutation(parent,lbound,ubound)
    dim = length(lbound);
    pm = 1/dim;
    offspring = parent;
    for i = 1:dim
        if rand < pm
            u = rand;
            if u <= 0.5
                delta = (2*u)^(1/(1+20));
            else
                delta = (1/(2*(1-u)))^(1/(1+20));
            end
            offspring(i) = parent(i) + delta*(ubound(i)-lbound(i));
            if offspring(i) < lbound(i)
                offspring(i) = lbound(i);
            elseif offspring(i) > ubound(i)
                offspring(i) = ubound(i);
            end
        end
    end
end

需要说明的是，这份代码示例只是一个简单的鲸鱼算法实现，并不是完整的建筑综合能源优化调度程序。如果您需要实现一个完整的建筑综合能源优化调度程序，可能需要更多的细节处理和专业知识。