写出基于遗传算法的蓄电池和飞轮储能的多目标容量优化配置matlab代码

以下是基于遗传算法的蓄电池和飞轮储能的多目标容量优化配置Matlab代码，供参考：

% 定义遗传算法参数
pop_size = 50; % 种群数量
cross_rate = 0.8; % 交叉概率
mutation_rate = 0.1; % 变异概率
n_generations = 50; % 迭代次数

% 定义系统参数
power_demand = 10; % 功率需求
energy_demand = 100; % 能量需求
battery_cost = 1; % 电池成本
flywheel_cost = 5; % 飞轮成本
battery_life = 5; % 电池寿命
flywheel_life = 10; % 飞轮寿命

% 定义电池和飞轮容量范围
battery_capacity_range = [10, 50];
flywheel_capacity_range = [5, 25];

% 定义目标函数
function [f1, f2] = obj_func(x)
    battery_capacity = x(1);
    flywheel_capacity = x(2);
    battery_energy = battery_capacity * battery_life;
    flywheel_energy = flywheel_capacity * flywheel_life;
    total_cost = battery_capacity * battery_cost + flywheel_capacity * flywheel_cost;
    f1 = total_cost;
    f2 = -(battery_energy + flywheel_energy - energy_demand);
end

% 定义约束条件函数
function [c, ceq] = con_func(x)
    battery_capacity = x(1);
    flywheel_capacity = x(2);
    c = [power_demand - (battery_capacity + flywheel_capacity)];
    ceq = [];
end

% 进行多目标遗传算法求解
options = optimoptions('gamultiobj', 'PopulationSize', pop_size, 'CrossoverFraction', cross_rate, 'MutationFcn', {@mutationadaptfeasible, mutation_rate}, 'MaxGenerations', n_generations, 'Display', 'iter');
[x, fval] = gamultiobj(@obj_func, 2, [], [], [], [], battery_capacity_range, flywheel_capacity_range, @con_func, options);

% 输出最优解
battery_capacity = x(1);
flywheel_capacity = x(2);
total_cost = fval(1);
energy_deviation = -fval(2);
fprintf('Battery Capacity: %f\n', battery_capacity);
fprintf('Flywheel Capacity: %f\n', flywheel_capacity);
fprintf('Total Cost: %f\n', total_cost);
fprintf('Energy Deviation: %f\n', energy_deviation);