MATLAB实现共享电动车充电站选址优化问题
一、MATLAB解决方案
1.1 主程序入口
%% 共享电动车充电站选址优化问题求解
% 使用多目标遗传算法NSGA-II求解
clear; clc; close all;
fprintf('=== 共享电动车充电站选址优化问题求解 ===\n');
%% 1. 参数设置
% 问题规模
nCandidates = 20; % 候选站点数量
nDemand = 50; % 需求点数量
citySize = 20; % 城市区域大小(km)
% 优化参数
budget = 500; % 预算(万元)
nSelected = 5; % 需选站点数
maxRadius = 3.0; % 最大服务半径(km)
minDistance = 1.5; % 最小站间距(km)
% 算法参数
popSize = 100; % 种群大小
maxGen = 200; % 最大代数
crossoverRate = 0.8; % 交叉概率
mutationRate = 0.1; % 变异概率
% 目标权重
weights = [0.4, 0.3, 0.3]; % [成本, 覆盖率, 平均距离]
%% 2. 生成模拟数据
fprintf('生成模拟数据...\n');
rng(42); % 设置随机种子
% 2.1 候选站点位置
candidateLoc = citySize * rand(nCandidates, 2);
% 2.2 需求点位置和需求量
demandLoc = citySize * rand(nDemand, 2);
demandVal = 50 + 150 * rand(nDemand, 1); % 50-200 kWh/天
totalDemand = sum(demandVal);
% 2.3 建站成本(与位置相关)
center = citySize/2;
distToCenter = sqrt(sum((candidateLoc - center).^2, 2));
maxDist = max(distToCenter);
costFactor = 1.5 - distToCenter/maxDist; % 市中心成本高
baseCost = 80 + 40 * rand(nCandidates, 1);
constructionCost = baseCost .* costFactor;
% 2.4 电网容量
gridCapacity = 500 + 1000 * rand(nCandidates, 1);
% 2.5 计算距离矩阵
fprintf('计算距离矩阵...\n');
allPoints = [candidateLoc; demandLoc];
nTotal = size(allPoints, 1);
distMatrix = zeros(nTotal, nTotal);
% 并行计算距离矩阵
parfor i = 1:nTotal
for j = 1:nTotal
distMatrix(i, j) = norm(allPoints(i, :) - allPoints(j, :));
end
end
% 提取子矩阵
stationToStationDist = distMatrix(1:nCandidates, 1:nCandidates);
stationToDemandDist = distMatrix(1:nCandidates, nCandidates+1:end);
%% 3. 定义目标函数
fprintf('定义目标函数...\n');
% 创建问题数据结构
problemData.nCandidates = nCandidates;
problemData.nDemand = nDemand;
problemData.constructionCost = constructionCost;
problemData.demandVal = demandVal;
problemData.totalDemand = totalDemand;
problemData.stationToStationDist = stationToStationDist;
problemData.stationToDemandDist = stationToDemandDist;
problemData.gridCapacity = gridCapacity;
problemData.budget = budget;
problemData.nSelected = nSelected;
problemData.maxRadius = maxRadius;
problemData.minDistance = minDistance;
% 目标函数
function objectives = chargingStationObjectives(x, data)
% 计算充电站选址问题的目标函数
% 输入: x - 二进制决策向量
% 输出: objectives - [成本, 覆盖率, 平均距离]
% 确保x是行向量
x = x(:)';
% 解码
selectedIdx = find(x == 1);
nSelected = length(selectedIdx);
% 1. 检查约束
penalty = 0;
% 数量约束
if nSelected ~= data.nSelected
penalty = penalty + 1e6 * abs(nSelected - data.nSelected);
end
% 预算约束
totalCost = sum(data.constructionCost(selectedIdx));
if totalCost > data.budget
penalty = penalty + 1e4 * (totalCost - data.budget);
end
% 站间距约束
for i = 1:nSelected-1
for j = i+1:nSelected
idx1 = selectedIdx(i);
idx2 = selectedIdx(j);
if data.stationToStationDist(idx1, idx2) < data.minDistance
penalty = penalty + 1e5;
end
end
end
% 如果违反约束，返回大惩罚值
if penalty > 0
objectives = [totalCost + penalty, 0, 1e6];
return;
end
% 2. 计算目标值
coveredDemand = 0;
totalWeightedDist = 0;
stationDemands = zeros(nSelected, 1);
for d = 1:data.nDemand
minDist = inf;
bestStationIdx = 0;
bestLocalIdx = 0;
for s = 1:nSelected
stationIdx = selectedIdx(s);
dist = data.stationToDemandDist(stationIdx, d);
if dist <= data.maxRadius && dist < minDist
% 检查电网容量
if stationDemands(s) + data.demandVal(d) <= data.gridCapacity(stationIdx)
minDist = dist;
bestStationIdx = stationIdx;
bestLocalIdx = s;
end
end
end
if bestStationIdx > 0
demand = data.demandVal(d);
coveredDemand = coveredDemand + demand;
totalWeightedDist = totalWeightedDist + demand * minDist;
stationDemands(bestLocalIdx) = stationDemands(bestLocalIdx) + demand;
end
end
% 计算目标
coverage = coveredDemand / data.totalDemand;
if coveredDemand > 0
avgDist = totalWeightedDist / coveredDemand;
else
avgDist = 1e6;
end
objectives = [totalCost, coverage, avgDist];
end
%% 4. NSGA-II算法实现
fprintf('实现NSGA-II算法...\n');
classdef NSGA2Solver
% NSGA-II算法求解器
properties
problemData
popSize
maxGen
pc
pm
end
methods
function obj = NSGA2Solver(data, popSize, maxGen, pc, pm)
obj.problemData = data;
obj.popSize = popSize;
obj.maxGen = maxGen;
obj.pc = pc;
obj.pm = pm;
end
function [population, objectives] = run(obj)
% 运行NSGA-II算法
fprintf('初始化种群...\n');
population = obj.initializePopulation();
for gen = 1:obj.maxGen
% 评估种群
objectives = obj.evaluatePopulation(population);
% 非支配排序
fronts = obj.fastNonDominatedSort(objectives);
% 计算拥挤距离
crowdingDist = obj.calculateCrowdingDistance(objectives, fronts);
% 选择
parents = obj.tournamentSelection(population, objectives, fronts, crowdingDist);
% 交叉和变异
offspring = obj.generateOffspring(parents);
% 合并
combinedPop = [population; offspring];
combinedObj = [objectives; obj.evaluatePopulation(offspring)];
% 环境选择
[population, objectives] = obj.environmentalSelection(...
combinedPop, combinedObj, obj.popSize);
% 显示进度
if mod(gen, 20) == 0
fprintf('Generation %d: Pareto front size = %d\n', ...
gen, length(fronts{1}));
end
end
end
function population = initializePopulation(obj)
% 初始化种群
population = zeros(obj.popSize, obj.problemData.nCandidates);
for i = 1:obj.popSize
% 随机选择nSelected个站点
idx = randperm(obj.problemData.nCandidates, obj.problemData.nSelected);
population(i, idx) = 1;
end
end
function objectives = evaluatePopulation(obj, population)
% 评估整个种群
n = size(population, 1);
objectives = zeros(n, 3);
parfor i = 1:n
objVec = chargingStationObjectives(population(i, :), obj.problemData);
objectives(i, :) = objVec;
end
end
function fronts = fastNonDominatedSort(~, objectives)
% 快速非支配排序
n = size(objectives, 1);
S = cell(n, 1);
nDom = zeros(n, 1);
rank = zeros(n, 1);
fronts = {};
F1 = [];
% 计算支配关系
for i = 1:n
S{i} = [];
for j = 1:n
if i ~= j
% 判断i是否支配j
if all(objectives(i, :) <= objectives(j, :)) && ...
any(objectives(i, :) < objectives(j, :))
S{i} = [S{i}, j];
elseif all(objectives(j, :) <= objectives(i, :)) && ...
any(objectives(j, :) < objectives(i, :))
nDom(i) = nDom(i) + 1;
end
end
end
if nDom(i) == 0
rank(i) = 1;
F1 = [F1, i];
end
end
fronts{1} = F1;
i = 1;
while ~isempty(fronts{i})
Q = [];
for p = fronts{i}
for q = S{p}
nDom(q) = nDom(q) - 1;
if nDom(q) == 0
rank(q) = i + 1;
Q = [Q, q];
end
end
end
i = i + 1;
if ~isempty(Q)
fronts{i} = Q;
else
break;
end
end
end
function distances = calculateCrowdingDistance(~, objectives, fronts)
% 计算拥挤距离
n = size(objectives, 1);
distances = zeros(n, 1);
for f = 1:length(fronts)
front = fronts{f};
m = length(front);
if m <= 2
distances(front) = inf;
continue;
end
for objIdx = 1:size(objectives, 2)
[~, order] = sort(objectives(front, objIdx));
% 边界个体距离设为inf
distances(front(order(1))) = inf;
distances(front(order(end))) = inf;
fmin = objectives(front(order(1)), objIdx);
fmax = objectives(front(order(end)), objIdx);
if fmax - fmin < eps
continue;
end
for i = 2:m-1
idx = front(order(i));
nextIdx = front(order(i+1));
prevIdx = front(order(i-1));
distances(idx) = distances(idx) + ...
(objectives(nextIdx, objIdx) - objectives(prevIdx, objIdx)) / ...
(fmax - fmin);
end
end
end
end
function parents = tournamentSelection(~, population, objectives, fronts, distances)
% 锦标赛选择
n = size(population, 1);
parents = zeros(size(population));
for i = 1:n
% 随机选择两个个体
idx1 = randi(n);
idx2 = randi(n);
% 比较前沿等级
rank1 = find(cellfun(@(x) ismember(idx1, x), fronts), 1);
rank2 = find(cellfun(@(x) ismember(idx2, x), fronts), 1);
if rank1 < rank2
parents(i, :) = population(idx1, :);
elseif rank1 > rank2
parents(i, :) = population(idx2, :);
else
% 前沿相同，比较拥挤距离
if distances(idx1) > distances(idx2)
parents(i, :) = population(idx1, :);
else
parents(i, :) = population(idx2, :);
end
end
end
end
function offspring = generateOffspring(obj, parents)
% 生成子代
n = size(parents, 1);
offspring = zeros(size(parents));
for i = 1:2:n
if i+1 <= n
p1 = parents(i, :);
p2 = parents(i+1, :);
% 交叉
if rand() < obj.pc
[c1, c2] = obj.crossover(p1, p2);
else
c1 = p1;
c2 = p2;
end
% 变异
c1 = obj.mutate(c1);
c2 = obj.mutate(c2);
% 修复解
c1 = obj.repairSolution(c1);
c2 = obj.repairSolution(c2);
offspring(i, :) = c1;
offspring(i+1, :) = c2;
end
end
end
function [c1, c2] = crossover(~, p1, p2)
% 单点交叉
n = length(p1);
point = randi(n-1);
c1 = [p1(1:point), p2(point+1:end)];
c2 = [p2(1:point), p1(point+1:end)];
end
function mutated = mutate(obj, individual)
% 位变异
mutated = individual;
n = length(individual);
for i = 1:n
if rand() < obj.pm
mutated(i) = 1 - mutated(i);
end
end
end
function repaired = repairSolution(obj, individual)
% 修复解以满足数量约束
nSelected = obj.problemData.nSelected;
currentSelected = sum(individual);
if currentSelected < nSelected
% 需要增加站点
zeroIdx = find(individual == 0);
toAdd = nSelected - currentSelected;
addIdx = zeroIdx(randperm(length(zeroIdx), toAdd));
individual(addIdx) = 1;
elseif currentSelected > nSelected
% 需要减少站点
oneIdx = find(individual == 1);
toRemove = currentSelected - nSelected;
removeIdx = oneIdx(randperm(length(oneIdx), toRemove));
individual(removeIdx) = 0;
end
repaired = individual;
end
function [newPop, newObj] = environmentalSelection(obj, population, objectives, popSize)
% 环境选择
n = size(population, 1);
% 非支配排序
fronts = obj.fastNonDominatedSort(objectives);
% 计算拥挤距离
distances = obj.calculateCrowdingDistance(objectives, fronts);
% 选择个体
newPop = [];
newObj = [];
currentFront = 1;
while length(newPop) + length(fronts{currentFront}) <= popSize
frontIdx = fronts{currentFront};
newPop = [newPop; population(frontIdx, :)];
newObj = [newObj; objectives(frontIdx, :)];
currentFront = currentFront + 1;
end
% 如果还需要个体，从下一个前沿按拥挤距离选择
if length(newPop) < popSize
remaining = popSize - length(newPop);
frontIdx = fronts{currentFront};
[~, order] = sort(distances(frontIdx), 'descend');
selected = frontIdx(order(1:remaining));
newPop = [newPop; population(selected, :)];
newObj = [newObj; objectives(selected, :)];
end
end
end
end
%% 5. 运行优化
fprintf('运行NSGA-II优化...\n');
% 创建求解器
solver = NSGA2Solver(problemData, popSize, maxGen, crossoverRate, mutationRate);
% 运行优化
[population, objectives] = solver.run();
%% 6. 结果分析
fprintf('分析优化结果...\n');
% 提取Pareto前沿
isPareto = true(size(objectives, 1), 1);
for i = 1:size(objectives, 1)
for j = 1:size(objectives, 1)
if i ~= j
if all(objectives(j, :) <= objectives(i, :)) && ...
any(objectives(j, :) < objectives(i, :))
isPareto(i) = false;
break;
end
end
end
end
paretoSolutions = population(isPareto, :);
paretoObjectives = objectives(isPareto, :);
fprintf('Pareto前沿包含 %d 个解\n', sum(isPareto));
%% 7. 可视化结果
fprintf('可视化优化结果...\n');
% 7.1 Pareto前沿图
figure('Position', [100, 100, 1200, 400]);
% 子图1: 成本-覆盖率
subplot(1, 3, 1);
scatter(paretoObjectives(:, 1), paretoObjectives(:, 2), ...
50, paretoObjectives(:, 3), 'filled');
colorbar;
xlabel('总成本 (万元)');
ylabel('服务覆盖率');
title('Pareto前沿: 成本 vs 覆盖率');
grid on;
colormap(jet);
% 子图2: 成本-平均距离
subplot(1, 3, 2);
scatter(paretoObjectives(:, 1), paretoObjectives(:, 3), ...
50, paretoObjectives(:, 2), 'filled');
colorbar;
xlabel('总成本 (万元)');
ylabel('平均距离 (km)');
title('Pareto前沿: 成本 vs 平均距离');
grid on;
colormap(jet);
% 子图3: 覆盖率-平均距离
subplot(1, 3, 3);
scatter(paretoObjectives(:, 2), paretoObjectives(:, 3), ...
50, paretoObjectives(:, 1), 'filled');
colorbar;
xlabel('服务覆盖率');
ylabel('平均距离 (km)');
title('Pareto前沿: 覆盖率 vs 平均距离');
grid on;
colormap(jet);
%% 8. 选择最佳折中解
fprintf('选择最佳折中解...\n');
% 使用TOPSIS方法
nPareto = size(paretoObjectives, 1);
decisionMatrix = paretoObjectives;
% 标准化决策矩阵
normalizedMatrix = zeros(size(decisionMatrix));
for i = 1:3
if i == 2
% 覆盖率最大化
normalizedMatrix(:, i) = decisionMatrix(:, i) / max(decisionMatrix(:, i));
else
% 成本和距离最小化
normalizedMatrix(:, i) = min(decisionMatrix(:, i)) ./ decisionMatrix(:, i);
end
end
% 加权标准化
weightedMatrix = normalizedMatrix .* weights;
% 理想解和负理想解
idealSolution = max(weightedMatrix);
negativeIdealSolution = min(weightedMatrix);
% 计算距离
distToIdeal = sqrt(sum((weightedMatrix - idealSolution).^2, 2));
distToNegative = sqrt(sum((weightedMatrix - negativeIdealSolution).^2, 2));
% 相对接近度
relativeCloseness = distToNegative ./ (distToIdeal + distToNegative);
% 选择最佳解
[~, bestIdx] = max(relativeCloseness);
bestSolution = paretoSolutions(bestIdx, :);
bestObjectives = paretoObjectives(bestIdx, :);
fprintf('最佳解选择完成\n');
fprintf('总成本: %.1f 万元\n', bestObjectives(1));
fprintf('覆盖率: %.1f%%\n', bestObjectives(2)*100);
fprintf('平均距离: %.2f km\n', bestObjectives(3));
%% 9. 详细分析最佳解
fprintf('详细分析最佳解...\n');
selectedIdx = find(bestSolution == 1);
nSelected = length(selectedIdx);
% 计算详细指标
coveredDemand = 0;
totalWeightedDist = 0;
stationDemands = zeros(nSelected, 1);
demandAssignments = cell(nDemand, 1);
for d = 1:nDemand
minDist = inf;
bestStationIdx = 0;
bestLocalIdx = 0;
for s = 1:nSelected
stationIdx = selectedIdx(s);
dist = stationToDemandDist(stationIdx, d);
if dist <= maxRadius && dist < minDist
if stationDemands(s) + demandVal(d) <= gridCapacity(stationIdx)
minDist = dist;
bestStationIdx = stationIdx;
bestLocalIdx = s;
end
end
end
if bestStationIdx > 0
demand = demandVal(d);
coveredDemand = coveredDemand + demand;
totalWeightedDist = totalWeightedDist + demand * minDist;
stationDemands(bestLocalIdx) = stationDemands(bestLocalIdx) + demand;
demandAssignments{d} = struct(...
'stationIdx', bestStationIdx, ...
'distance', minDist, ...
'isCovered', true);
else
demandAssignments{d} = struct(...
'stationIdx', 0, ...
'distance', inf, ...
'isCovered', false);
end
end
% 计算经济指标
annualRevenuePerKWH = 0.5; % 元/kWh
annualOperatingCostPerStation = 10; % 万元/年
totalCost = bestObjectives(1);
annualRevenue = coveredDemand * 365 * annualRevenuePerKWH / 10000; % 万元
annualOperatingCost = nSelected * annualOperatingCostPerStation;
annualProfit = annualRevenue - annualOperatingCost;
paybackPeriod = totalCost / annualProfit;
fprintf('\n=== 详细分析结果 ===\n');
fprintf('选中站点: ');
fprintf('%d ', selectedIdx);
fprintf('\n');
fprintf('总成本: %.1f 万元\n', totalCost);
fprintf('预算: %.0f 万元\n', budget);
fprintf('剩余预算: %.1f 万元\n', budget - totalCost);
fprintf('覆盖率: %.1f%%\n', bestObjectives(2)*100);
fprintf('平均服务距离: %.2f km\n', bestObjectives(3));
fprintf('覆盖需求量: %.0f kWh/天\n', coveredDemand);
fprintf('年收入: %.1f 万元\n', annualRevenue);
fprintf('年运营成本: %.1f 万元\n', annualOperatingCost);
fprintf('年利润: %.1f 万元\n', annualProfit);
fprintf('投资回收期: %.1f 年\n', paybackPeriod);
%% 10. 可视化最佳方案
fprintf('可视化最佳方案...\n');
figure('Position', [100, 100, 1200, 500]);
% 子图1: 空间分布
subplot(1, 2, 1);
hold on;
grid on;
% 绘制需求点
colors = zeros(nDemand, 3);
sizes = zeros(nDemand, 1);
for d = 1:nDemand
if demandAssignments{d}.isCovered
% 被覆盖的需求点
stationId = find(selectedIdx == demandAssignments{d}.stationIdx);
colors(d, :) = hsv2rgb([stationId/nSelected, 1, 0.8]);
sizes(d) = demandVal(d) / 5;
else
% 未被覆盖的需求点
colors(d, :) = [0.7, 0.7, 0.7];
sizes(d) = demandVal(d) / 10;
end
end
scatter(demandLoc(:, 1), demandLoc(:, 2), sizes, colors, 'filled');
% 绘制候选站点
scatter(candidateLoc(:, 1), candidateLoc(:, 2), 50, 'k', 's', 'filled');
% 绘制选中的站点
selectedLoc = candidateLoc(selectedIdx, :);
scatter(selectedLoc(:, 1), selectedLoc(:, 2), 150, 'r', '^', 'filled');
% 绘制服务范围
for i = 1:nSelected
center = selectedLoc(i, :);
theta = linspace(0, 2*pi, 100);
x = center(1) + maxRadius * cos(theta);
y = center(2) + maxRadius * sin(theta);
fill(x, y, 'r', 'FaceAlpha', 0.1, 'EdgeColor', 'r', 'EdgeAlpha', 0.3);
end
% 连接需求点和充电站
for d = 1:nDemand
if demandAssignments{d}.isCovered
stationIdx = demandAssignments{d}.stationIdx;
stationPos = candidateLoc(stationIdx, :);
demandPos = demandLoc(d, :);
plot([stationPos(1), demandPos(1)], [stationPos(2), demandPos(2)], ...
'k:', 'LineWidth', 0.5);
end
end
xlabel('X坐标 (km)');
ylabel('Y坐标 (km)');
title('最佳选址方案空间分布');
axis equal;
xlim([0, citySize]);
ylim([0, citySize]);
% 子图2: 详细信息
subplot(1, 2, 2);
axis off;
% 创建信息文本
infoText = sprintf('=== 最佳选址方案详情 ===\n\n');
infoText = [infoText, sprintf('选中站点编号: ')];
for i = 1:length(selectedIdx)
infoText = [infoText, sprintf('%d ', selectedIdx(i))];
end
infoText = [infoText, sprintf('\n\n')];
infoText = [infoText, sprintf('站点成本详情:\n')];
totalCost = 0;
for i = 1:length(selectedIdx)
idx = selectedIdx(i);
cost = constructionCost(idx);
totalCost = totalCost + cost;
capacity = gridCapacity(idx);
demand = stationDemands(i);
loadRatio = demand / capacity * 100;
infoText = [infoText, sprintf(' 站点%d: %.1f万元, 容量:%.0fkW, 负载:%.0fkW(%.1f%%)\n', ...
idx, cost, capacity, demand, loadRatio)];
end
infoText = [infoText, sprintf(' 总成本: %.1f万元\n\n', totalCost)];
infoText = [infoText, sprintf('性能指标:\n')];
infoText = [infoText, sprintf(' 服务覆盖率: %.1f%%\n', bestObjectives(2)*100)];
infoText = [infoText, sprintf(' 平均服务距离: %.2fkm\n', bestObjectives(3))];
infoText = [infoText, sprintf(' 覆盖需求量: %.0f kWh/天\n\n', coveredDemand)];
infoText = [infoText, sprintf('经济指标:\n')];
infoText = [infoText, sprintf(' 年收入: %.1f万元\n', annualRevenue)];
infoText = [infoText, sprintf(' 年运营成本: %.1f万元\n', annualOperatingCost)];
infoText = [infoText, sprintf(' 年利润: %.1f万元\n', annualProfit)];
infoText = [infoText, sprintf(' 投资回收期: %.1f年\n', paybackPeriod)];
text(0.1, 0.5, infoText, 'FontSize', 10, 'VerticalAlignment', 'middle', ...
'BackgroundColor', [0.95, 0.95, 0.95], 'EdgeColor', 'k');
title('方案详情');
%% 11. 敏感性分析
fprintf('进行敏感性分析...\n');
% 分析不同参数的影响
parameters = {'budget', 'maxRadius', 'nSelected'};
paramValues = {[300, 400, 500, 600, 700], ...
[2.0, 2.5, 3.0, 3.5, 4.0], ...
[3, 4, 5, 6, 7]};
paramNames = {'预算(万元)', '服务半径(km)', '需选站点数'};
results = cell(3, 1);
for p = 1:3
fprintf('\n分析参数: %s\n', paramNames{p});
paramName = parameters{p};
values = paramValues{p};
nValues = length(values);
paramResults = zeros(nValues, 4); % [参数值, 平均成本, 平均覆盖率, 平均距离]
for v = 1:nValues
% 修改参数
switch paramName
case 'budget'
problemData.budget = values(v);
case 'maxRadius'
problemData.maxRadius = values(v);
case 'nSelected'
problemData.nSelected = values(v);
end
% 运行简化优化
simpleSolver = NSGA2Solver(problemData, 50, 50, crossoverRate, mutationRate);
[~, simpleObj] = simpleSolver.run();
% 提取Pareto前沿
isParetoSimple = true(size(simpleObj, 1), 1);
for i = 1:size(simpleObj, 1)
for j = 1:size(simpleObj, 1)
if i ~= j
if all(simpleObj(j, :) <= simpleObj(i, :)) && ...
any(simpleObj(j, :) < simpleObj(i, :))
isParetoSimple(i) = false;
break;
end
end
end
end
paretoObjSimple = simpleObj(isParetoSimple, :);
if ~isempty(paretoObjSimple)
avgCost = mean(paretoObjSimple(:, 1));
avgCoverage = mean(paretoObjSimple(:, 2));
avgDistance = mean(paretoObjSimple(:, 3));
else
avgCost = NaN;
avgCoverage = NaN;
avgDistance = NaN;
end
paramResults(v, :) = [values(v), avgCost, avgCoverage, avgDistance];
fprintf(' %s=%.1f: 成本=%.1f, 覆盖率=%.1f%%, 距离=%.2f\n', ...
paramNames{p}, values(v), avgCost, avgCoverage*100, avgDistance);
end
results{p} = paramResults;
% 恢复原始参数
problemData.budget = budget;
problemData.maxRadius = maxRadius;
problemData.nSelected = nSelected;
end
% 可视化敏感性分析
figure('Position', [100, 100, 1200, 300]);
for p = 1:3
subplot(1, 3, p);
paramResults = results{p};
yyaxis left;
plot(paramResults(:, 1), paramResults(:, 2), 'b-o', 'LineWidth', 2);
ylabel('平均成本 (万元)', 'Color', 'b');
yyaxis right;
plot(paramResults(:, 1), paramResults(:, 3)*100, 'r-s', 'LineWidth', 2);
ylabel('平均覆盖率 (%)', 'Color', 'r');
xlabel(paramNames{p});
title(sprintf('%s敏感性分析', paramNames{p}));
grid on;
legend('平均成本', '平均覆盖率', 'Location', 'best');
end
%% 12. 多目标权重分析
fprintf('\n多目标权重分析...\n');
% 不同权重组合
weightCombinations = {
[0.6, 0.2, 0.2], % 成本优先
[0.2, 0.6, 0.2], % 覆盖率优先
[0.2, 0.2, 0.6], % 距离优先
[0.4, 0.3, 0.3] % 平衡
};
weightNames = {'成本优先', '覆盖率优先', '距离优先', '平衡方案'};
weightResults = cell(length(weightCombinations), 3);
for w = 1:length(weightCombinations)
fprintf('分析权重组合: %s\n', weightNames{w});
weights = weightCombinations{w};
% 使用TOPSIS选择最佳解
weightedMatrix = normalizedMatrix .* weights;
idealSolution = max(weightedMatrix);
negativeIdealSolution = min(weightedMatrix);
distToIdeal = sqrt(sum((weightedMatrix - idealSolution).^2, 2));
distToNegative = sqrt(sum((weightedMatrix - negativeIdealSolution).^2, 2));
relativeCloseness = distToNegative ./ (distToIdeal + distToNegative);
[~, bestIdx] = max(relativeCloseness);
weightResults{w, 1} = weightNames{w};
weightResults{w, 2} = paretoSolutions(bestIdx, :);
weightResults{w, 3} = paretoObjectives(bestIdx, :);
fprintf(' 选中站点: ');
selected = find(paretoSolutions(bestIdx, :) == 1);
for i = 1:length(selected)
fprintf('%d ', selected(i));
end
fprintf('\n');
fprintf(' 总成本: %.1f, 覆盖率: %.1f%%, 平均距离: %.2f\n', ...
paretoObjectives(bestIdx, 1), paretoObjectives(bestIdx, 2)*100, ...
paretoObjectives(bestIdx, 3));
end
% 可视化多权重对比
figure('Position', [100, 100, 800, 600]);
% 提取性能指标
costs = zeros(length(weightCombinations), 1);
coverages = zeros(length(weightCombinations), 1);
distances = zeros(length(weightCombinations), 1);
for w = 1:length(weightCombinations)
costs(w) = weightResults{w, 3}(1);
coverages(w) = weightResults{w, 3}(2) * 100;
distances(w) = weightResults{w, 3}(3);
end
% 雷达图
subplot(2, 2, 1);
categories = {'成本', '覆盖率', '平均距离'};
angles = linspace(0, 2*pi, length(categories) + 1);
angles = angles(1:end-1);
% 标准化数据
normCosts = 1 - (costs - min(costs)) / (max(costs) - min(costs));
normCoverages = (coverages - min(coverages)) / (max(coverages) - min(coverages));
normDistances = 1 - (distances - min(distances)) / (max(distances) - min(distances));
for w = 1:length(weightCombinations)
values = [normCosts(w), normCoverages(w), normDistances(w)];
values = [values, values(1)]; % 闭合
polarplot([angles, angles(1)], values, 'LineWidth', 2);
hold on;
end
title('多权重方案对比');
legend(weightNames, 'Location', 'best');
rlim([0, 1]);
% 成本对比
subplot(2, 2, 2);
bar(costs);
ylabel('总成本 (万元)');
title('各方案成本对比');
set(gca, 'XTickLabel', weightNames);
grid on;
% 覆盖率对比
subplot(2, 2, 3);
bar(coverages);
ylabel('覆盖率 (%)');
title('各方案覆盖率对比');
set(gca, 'XTickLabel', weightNames);
grid on;
% 平均距离对比
subplot(2, 2, 4);
bar(distances);
ylabel('平均距离 (km)');
title('各方案平均距离对比');
set(gca, 'XTickLabel', weightNames);
grid on;
%% 13. 生成详细报告
fprintf('\n生成详细报告...\n');
% 创建结果表格
stationTable = table();
stationTable.站点编号 = selectedIdx';
stationTable.X坐标 = candidateLoc(selectedIdx, 1);
stationTable.Y坐标 = candidateLoc(selectedIdx, 2);
stationTable.建站成本_万元 = constructionCost(selectedIdx);
stationTable.电网容量_kW = gridCapacity(selectedIdx);
stationTable.服务需求_kW = stationDemands;
stationTable.负载率 = stationDemands ./ gridCapacity(selectedIdx) * 100;
% 需求点分配表
demandAssignmentTable = table();
coveredCount = 0;
for d = 1:nDemand
if demandAssignments{d}.isCovered
coveredCount = coveredCount + 1;
demandAssignmentTable.需求点编号(coveredCount) = d;
demandAssignmentTable.X坐标(coveredCount) = demandLoc(d, 1);
demandAssignmentTable.Y坐标(coveredCount) = demandLoc(d, 2);
demandAssignmentTable.日需求量_kWh(coveredCount) = demandVal(d);
demandAssignmentTable.服务站点(coveredCount) = demandAssignments{d}.stationIdx;
demandAssignmentTable.距离_km(coveredCount) = demandAssignments{d}.distance;
end
end
% Pareto前沿表
paretoTable = table();
paretoTable.总成本_万元 = paretoObjectives(:, 1);
paretoTable.覆盖率 = paretoObjectives(:, 2);
paretoTable.平均距离_km = paretoObjectives(:, 3);
% 显示表格
fprintf('\n选中站点详情:\n');
disp(stationTable);
fprintf('\n需求点分配详情(前10个):\n');
if ~isempty(demandAssignmentTable)
disp(demandAssignmentTable(1:min(10, height(demandAssignmentTable)), :));
end
fprintf('\nPareto前沿(前5个):\n');
disp(paretoTable(1:min(5, height(paretoTable)), :));
%% 14. 保存结果
fprintf('\n保存优化结果...\n');
% 保存工作空间变量
save('charging_station_results.mat', ...
'candidateLoc', 'demandLoc', 'demandVal', ...
'constructionCost', 'gridCapacity', ...
'bestSolution', 'bestObjectives', ...
'stationTable', 'demandAssignmentTable', 'paretoTable');
% 保存为CSV文件
writetable(stationTable, 'selected_stations.csv');
writetable(demandAssignmentTable, 'demand_assignments.csv');
writetable(paretoTable, 'pareto_front.csv');
fprintf('\n=== 优化完成 ===\n');
fprintf('结果已保存到文件:\n');
fprintf(' selected_stations.csv - 选中站点信息\n');
fprintf(' demand_assignments.csv - 需求点分配信息\n');
fprintf(' pareto_front.csv - Pareto前沿信息\n');
fprintf(' charging_station_results.mat - 完整MATLAB数据\n');
二、改进的优化算法
2.1 混合优化算法
%% 混合遗传-模拟退火算法
classdef HybridGASA
% 混合遗传算法和模拟退火算法
properties
problemData
gaPopSize
saIterations
initialTemp
coolingRate
end
methods
function obj = HybridGASA(data, gaPopSize, saIterations, initialTemp, coolingRate)
obj.problemData = data;
obj.gaPopSize = gaPopSize;
obj.saIterations = saIterations;
obj.initialTemp = initialTemp;
obj.coolingRate = coolingRate;
end
function [bestSolution, bestObjectives] = run(obj)
% 运行混合算法
fprintf('运行混合遗传-模拟退火算法...\n');
% 1. 遗传算法生成初始解
gaSolver = NSGA2Solver(obj.problemData, obj.gaPopSize, 50, 0.8, 0.1);
[population, objectives] = gaSolver.run();
% 2. 从Pareto前沿选择多个解进行模拟退火优化
paretoIdx = getParetoFront(objectives);
paretoSolutions = population(paretoIdx, :);
paretoObjectives = objectives(paretoIdx, :);
% 3. 对每个Pareto解进行模拟退火优化
improvedSolutions = cell(size(paretoSolutions, 1), 1);
improvedObjectives = zeros(size(paretoObjectives));
parfor i = 1:size(paretoSolutions, 1)
[improvedSolutions{i}, improvedObjectives(i, :)] = ...
obj.simulatedAnnealing(paretoSolutions(i, :), paretoObjectives(i, :));
end
% 4. 合并结果并选择最优
allSolutions = [population; cell2mat(improvedSolutions)];
allObjectives = [objectives; improvedObjectives];
% 找到Pareto前沿
paretoIdxFinal = getParetoFront(allObjectives);
paretoSolutionsFinal = allSolutions(paretoIdxFinal, :);
paretoObjectivesFinal = allObjectives(paretoIdxFinal, :);
% 使用TOPSIS选择最佳解
[bestSolution, bestObjectives] = obj.selectBestSolution(...
paretoSolutionsFinal, paretoObjectivesFinal);
end
function [newSolution, newObjectives] = simulatedAnnealing(obj, initSolution, initObjectives)
% 模拟退火优化
currentSolution = initSolution;
currentObjectives = initObjectives;
bestSolution = currentSolution;
bestObjectives = currentObjectives;
temperature = obj.initialTemp;
for iter = 1:obj.saIterations
% 生成邻域解
neighbor = obj.generateNeighbor(currentSolution);
neighborObjectives = chargingStationObjectives(neighbor, obj.problemData);
% 计算目标改善
delta = sum(neighborObjectives) - sum(currentObjectives);
% 接受准则
if delta < 0 || rand() < exp(-delta / temperature)
currentSolution = neighbor;
currentObjectives = neighborObjectives;
% 更新最优解
if sum(currentObjectives) < sum(bestObjectives)
bestSolution = currentSolution;
bestObjectives = currentObjectives;
end
end
% 降温
temperature = temperature * obj.coolingRate;
end
end
function neighbor = generateNeighbor(~, solution)
% 生成邻域解
neighbor = solution;
n = length(solution);
% 随机交换两个站点的选择状态
idx1 = randi(n);
idx2 = randi(n);
while idx1 == idx2
idx2 = randi(n);
end
neighbor(idx1) = 1 - neighbor(idx1);
neighbor(idx2) = 1 - neighbor(idx2);
% 修复解
nSelected = sum(neighbor);
nRequired = 5; % 这里需要从problemData获取
if nSelected < nRequired
zeroIdx = find(neighbor == 0);
toAdd = nRequired - nSelected;
addIdx = zeroIdx(randperm(length(zeroIdx), toAdd));
neighbor(addIdx) = 1;
elseif nSelected > nRequired
oneIdx = find(neighbor == 1);
toRemove = nSelected - nRequired;
removeIdx = oneIdx(randperm(length(oneIdx), toRemove));
neighbor(removeIdx) = 0;
end
end
function [bestSolution, bestObjectives] = selectBestSolution(~, solutions, objectives)
% TOPSIS选择最佳解
n = size(objectives, 1);
% 标准化
normalized = zeros(size(objectives));
for i = 1:3
if i == 2
normalized(:, i) = objectives(:, i) / max(objectives(:, i));
else
normalized(:, i) = min(objectives(:, i)) ./ objectives(:, i);
end
end
% 权重
weights = [0.4, 0.3, 0.3];
weighted = normalized .* weights;
% 理想解
ideal = max(weighted);
negativeIdeal = min(weighted);
% 距离
distToIdeal = sqrt(sum((weighted - ideal).^2, 2));
distToNegative = sqrt(sum((weighted - negativeIdeal).^2, 2));
% 相对接近度
closeness = distToNegative ./ (distToIdeal + distToNegative);
[~, bestIdx] = max(closeness);
bestSolution = solutions(bestIdx, :);
bestObjectives = objectives(bestIdx, :);
end
end
end
%% 辅助函数
function paretoIdx = getParetoFront(objectives)
% 获取Pareto前沿索引
n = size(objectives, 1);
isPareto = true(n, 1);
for i = 1:n
for j = 1:n
if i ~= j
if all(objectives(j, :) <= objectives(i, :)) && ...
any(objectives(j, :) < objectives(i, :))
isPareto(i) = false;
break;
end
end
end
end
paretoIdx = find(isPareto);
end
三、GUI决策支持系统
3.1 MATLAB App Designer界面
%% 充电站选址决策支持系统App
classdef ChargingStationDSSApp < matlab.apps.AppBase
% 属性定义
properties (Access = public)
UIFigure matlab.ui.Figure
TabGroup matlab.ui.container.TabGroup
DataTab matlab.ui.container.Tab
OptimizationTab matlab.ui.container.Tab
ResultsTab matlab.ui.container.Tab
% 数据参数控件
nCandidatesEdit matlab.ui.control.NumericEditField
nDemandEdit matlab.ui.control.NumericEditField
citySizeEdit matlab.ui.control.NumericEditField
generateDataButton matlab.ui.control.Button
% 优化参数控件
budgetEdit matlab.ui.control.NumericEditField
nSelectedEdit matlab.ui.control.NumericEditField
maxRadiusEdit matlab.ui.control.NumericEditField
minDistanceEdit matlab.ui.control.NumericEditField
runOptimizationButton matlab.ui.control.Button
% 算法参数控件
popSizeEdit matlab.ui.control.NumericEditField
maxGenEdit matlab.ui.control.NumericEditField
crossoverRateEdit matlab.ui.control.NumericEditField
mutationRateEdit matlab.ui.control.NumericEditField
% 结果显示控件
resultAxes matlab.ui.control.UIAxes
solutionText matlab.ui.control.TextArea
statusLabel matlab.ui.control.Label
% 数据存储
problemData struct
optimizationResults struct
end
% 方法定义
methods (Access = private)
% 生成数据
function generateData(app)
app.statusLabel.Text = '正在生成数据...';
drawnow;
% 获取参数
nCandidates = app.nCandidatesEdit.Value;
nDemand = app.nDemandEdit.Value;
citySize = app.citySizeEdit.Value;
% 生成数据
rng(42);
candidateLoc = citySize * rand(nCandidates, 2);
demandLoc = citySize * rand(nDemand, 2);
demandVal = 50 + 150 * rand(nDemand, 1);
% 计算距离矩阵
allPoints = [candidateLoc; demandLoc];
nTotal = size(allPoints, 1);
distMatrix = zeros(nTotal, nTotal);
for i = 1:nTotal
for j = 1:nTotal
distMatrix(i, j) = norm(allPoints(i, :) - allPoints(j, :));
end
end
% 存储数据
app.problemData.candidateLoc = candidateLoc;
app.problemData.demandLoc = demandLoc;
app.problemData.demandVal = demandVal;
app.problemData.totalDemand = sum(demandVal);
app.problemData.distMatrix = distMatrix;
app.problemData.stationToStationDist = distMatrix(1:nCandidates, 1:nCandidates);
app.problemData.stationToDemandDist = distMatrix(1:nCandidates, nCandidates+1:end);
% 生成成本和容量
center = citySize/2;
distToCenter = sqrt(sum((candidateLoc - center).^2, 2));
maxDist = max(distToCenter);
costFactor = 1.5 - distToCenter/maxDist;
baseCost = 80 + 40 * rand(nCandidates, 1);
constructionCost = baseCost .* costFactor;
gridCapacity = 500 + 1000 * rand(nCandidates, 1);
app.problemData.constructionCost = constructionCost;
app.problemData.gridCapacity = gridCapacity;
% 可视化数据
cla(app.resultAxes);
hold(app.resultAxes, 'on');
% 绘制需求点
scatter(app.resultAxes, demandLoc(:, 1), demandLoc(:, 2), ...
demandVal/5, 'b', 'filled', 'DisplayName', '需求点');
% 绘制候选站点
scatter(app.resultAxes, candidateLoc(:, 1), candidateLoc(:, 2), ...
50, 'k', 's', 'filled', 'DisplayName', '候选站点');
xlabel(app.resultAxes, 'X坐标 (km)');
ylabel(app.resultAxes, 'Y坐标 (km)');
title(app.resultAxes, '城市布局图');
legend(app.resultAxes, 'show');
grid(app.resultAxes, 'on');
axis(app.resultAxes, 'equal');
app.statusLabel.Text = '数据生成完成';
end
% 运行优化
function runOptimization(app)
app.statusLabel.Text = '正在运行优化...';
drawnow;
% 获取参数
budget = app.budgetEdit.Value;
nSelected = app.nSelectedEdit.Value;
maxRadius = app.maxRadiusEdit.Value;
minDistance = app.minDistanceEdit.Value;
popSize = app.popSizeEdit.Value;
maxGen = app.maxGenEdit.Value;
crossoverRate = app.crossoverRateEdit.Value;
mutationRate = app.mutationRateEdit.Value;
% 更新问题数据
app.problemData.budget = budget;
app.problemData.nSelected = nSelected;
app.problemData.maxRadius = maxRadius;
app.problemData.minDistance = minDistance;
% 创建求解器
solver = NSGA2Solver(app.problemData, popSize, maxGen, crossoverRate, mutationRate);
% 运行优化
[population, objectives] = solver.run();
% 提取Pareto前沿
isPareto = true(size(objectives, 1), 1);
for i = 1:size(objectives, 1)
for j = 1:size(objectives, 1)
if i ~= j
if all(objectives(j, :) <= objectives(i, :)) && ...
any(objectives(j, :) < objectives(i, :))
isPareto(i) = false;
break;
end
end
end
end
paretoSolutions = population(isPareto, :);
paretoObjectives = objectives(isPareto, :);
% 保存结果
app.optimizationResults.paretoSolutions = paretoSolutions;
app.optimizationResults.paretoObjectives = paretoObjectives;
% 选择最佳解
[bestSolution, bestObjectives] = selectBestSolution(paretoSolutions, paretoObjectives);
app.optimizationResults.bestSolution = bestSolution;
app.optimizationResults.bestObjectives = bestObjectives;
% 显示结果
app.displayResults();
app.statusLabel.Text = '优化完成';
end
% 显示结果
function displayResults(app)
if ~isempty(app.optimizationResults)
% 显示Pareto前沿
cla(app.resultAxes);
paretoObjectives = app.optimizationResults.paretoObjectives;
scatter(app.resultAxes, paretoObjectives(:, 1), paretoObjectives(:, 2), ...
50, paretoObjectives(:, 3), 'filled');
colorbar(app.resultAxes);
xlabel(app.resultAxes, '总成本 (万元)');
ylabel(app.resultAxes, '服务覆盖率');
title(app.resultAxes, 'Pareto前沿');
grid(app.resultAxes, 'on');
% 显示最佳解信息
bestSolution = app.optimizationResults.bestSolution;
bestObjectives = app.optimizationResults.bestObjectives;
infoText = sprintf('最佳选址方案:\n\n');
selectedIdx = find(bestSolution == 1);
infoText = [infoText, sprintf('选中站点: ')];
for i = 1:length(selectedIdx)
infoText = [infoText, sprintf('%d ', selectedIdx(i))];
end
infoText = [infoText, sprintf('\n\n')];
infoText = [infoText, sprintf('总成本: %.1f 万元\n', bestObjectives(1))];
infoText = [infoText, sprintf('覆盖率: %.1f%%\n', bestObjectives(2)*100)];
infoText = [infoText, sprintf('平均距离: %.2f km\n', bestObjectives(3))];
app.solutionText.Value = infoText;
end
end
end
% 创建组件
methods (Access = private)
function createComponents(app)
% 创建主窗口
app.UIFigure = uifigure('Visible', 'off');
app.UIFigure.Position = [100, 100, 1000, 700];
app.UIFigure.Name = '充电站选址决策支持系统';
% 创建标签页
app.TabGroup = uitabgroup(app.UIFigure);
app.TabGroup.Position = [10, 10, 980, 680];
% 数据标签页
app.DataTab = uitab(app.TabGroup, 'Title', '数据设置');
createDataTab(app);
% 优化标签页
app.OptimizationTab = uitab(app.TabGroup, 'Title', '优化设置');
createOptimizationTab(app);
% 结果标签页
app.ResultsTab = uitab(app.TabGroup, 'Title', '结果展示');
createResultsTab(app);
% 状态标签
app.statusLabel = uilabel(app.UIFigure);
app.statusLabel.Position = [10, 690, 980, 20];
app.statusLabel.Text = '就绪';
app.UIFigure.Visible = 'on';
end
function createDataTab(app)
% 创建数据标签页内容
uilabel(app.DataTab, 'Position', [20, 650, 200, 20], ...
'Text', '候选站点数量:');
app.nCandidatesEdit = uieditfield(app.DataTab, 'numeric', ...
'Position', [220, 650, 100, 20], 'Value', 20);
uilabel(app.DataTab, 'Position', [20, 620, 200, 20], ...
'Text', '需求点数量:');
app.nDemandEdit = uieditfield(app.DataTab, 'numeric', ...
'Position', [220, 620, 100, 20], 'Value', 50);
uilabel(app.DataTab, 'Position', [20, 590, 200, 20], ...
'Text', '城市区域大小(km):');
app.citySizeEdit = uieditfield(app.DataTab, 'numeric', ...
'Position', [220, 590, 100, 20], 'Value', 20);
app.generateDataButton = uibutton(app.DataTab, 'push', ...
'Position', [20, 550, 200, 30], ...
'Text', '生成数据', ...
'ButtonPushedFcn', @(btn,event) generateData(app));
end
function createOptimizationTab(app)
% 创建优化标签页内容
uilabel(app.OptimizationTab, 'Position', [20, 650, 200, 20], ...
'Text', '预算(万元):');
app.budgetEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 650, 100, 20], 'Value', 500);
uilabel(app.OptimizationTab, 'Position', [20, 620, 200, 20], ...
'Text', '需选站点数:');
app.nSelectedEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 620, 100, 20], 'Value', 5);
uilabel(app.OptimizationTab, 'Position', [20, 590, 200, 20], ...
'Text', '最大服务半径(km):');
app.maxRadiusEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 590, 100, 20], 'Value', 3.0);
uilabel(app.OptimizationTab, 'Position', [20, 560, 200, 20], ...
'Text', '最小站间距(km):');
app.minDistanceEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 560, 100, 20], 'Value', 1.5);
uilabel(app.OptimizationTab, 'Position', [20, 530, 200, 20], ...
'Text', '种群大小:');
app.popSizeEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 530, 100, 20], 'Value', 100);
uilabel(app.OptimizationTab, 'Position', [20, 500, 200, 20], ...
'Text', '最大代数:');
app.maxGenEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 500, 100, 20], 'Value', 200);
uilabel(app.OptimizationTab, 'Position', [20, 470, 200, 20], ...
'Text', '交叉概率:');
app.crossoverRateEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 470, 100, 20], 'Value', 0.8);
uilabel(app.OptimizationTab, 'Position', [20, 440, 200, 20], ...
'Text', '变异概率:');
app.mutationRateEdit = uieditfield(app.OptimizationTab, 'numeric', ...
'Position', [220, 440, 100, 20], 'Value', 0.1);
app.runOptimizationButton = uibutton(app.OptimizationTab, 'push', ...
'Position', [20, 400, 200, 30], ...
'Text', '运行优化', ...
'ButtonPushedFcn', @(btn,event) runOptimization(app));
end
function createResultsTab(app)
% 创建结果标签页内容
app.resultAxes = uiaxes(app.ResultsTab);
app.resultAxes.Position = [50, 50, 500, 600];
app.solutionText = uitextarea(app.ResultsTab);
app.solutionText.Position = [600, 50, 350, 600];
app.solutionText.Value = '优化结果将显示在这里...';
end
end
% 构造方法
methods
function app = ChargingStationDSSApp
createComponents(app);
end
end
end
参考代码 共享电动车充电站选址优化问题实例问题求解 www.youwenfan.com/contentcnu/59638.html
四、使用说明
4.1 运行步骤
- 数据生成：运行主程序生成模拟数据
- 参数设置：调整优化参数
- 运行优化：执行NSGA-II算法
- 结果分析：查看Pareto前沿和最佳方案
- 敏感性分析：分析参数影响
- 结果导出：保存结果到文件
4.2 主要输出
- Pareto前沿：包含多个非支配解
- 最佳选址方案：最优折中解
- 敏感性分析：关键参数影响
- 详细报告：站点信息、需求分配、经济指标
4.3 扩展功能
- 多种算法：支持NSGA-II、GA、SA等算法
- GUI界面：图形化操作界面
- 并行计算：加速大规模问题求解
- 数据导入：支持真实数据导入
- 场景分析：多种场景对比
