from this data, can you create a function to optimize the user comfort and energy consumption in smart building. I am trying to use this code to do multi objective optimization. can you try use my moema code. give me full code

here is the data:
% Data from UTHM 
% Time_instance Temp_(F) Temp_(C) lux  IAQ
dataSH=[    
    1.0000   60.0000   15.5600  711.0000  610.0000
    2.0000   65.0000   18.3300  717.0000  670.0000
    3.0000   67.0000   19.4400  718.0000  650.0000
    4.0000   66.0000   18.8900  717.0000  640.0000
    5.0000   67.0000   19.4400  700.0000  600.0000
    6.0000   63.0000   17.2200  713.0000  620.0000
    7.0000   62.0000   16.6700  724.0000  670.0000
    8.0000   81.0000   27.2200  710.0000  920.0000
    9.0000   80.0000   26.6700  914.0000  920.0000
   10.0000   81.0000   27.2200  900.0000  960.0000
   11.0000   83.0000   28.3300  897.0000  900.0000
   12.0000   80.0000   26.6700  895.0000  930.0000
   13.0000   79.0000   26.1100  904.0000  950.0000
   14.0000   82.0000   27.7800  903.0000  960.0000
   15.0000   81.0000   27.2200  906.0000  970.0000
   16.0000   80.0000   26.6700  898.0000  925.0000
   17.0000   66.0000   18.8900  728.0000  610.0000
   18.0000   63.0000   17.2200  703.0000  670.0000
   19.0000   66.0000   18.8900  714.0000  630.0000
   20.0000   66.0000   18.8900  721.0000  645.0000
   21.0000   66.0000   18.8900  722.0000  650.0000
   22.0000   67.0000   19.4400  703.0000  600.0000
   23.0000   66.0000   18.8900  701.0000  670.0000
   24.0000   81.0000   27.2200  728.0000  980.0000
   25.0000   81.0000   27.2200  891.0000  930.0000
   26.0000   82.0000   27.7800  906.0000  948.0000
   27.0000   80.0000   26.6700  901.0000  965.0000
   28.0000   81.0000   27.2200  901.0000  916.0000
   29.0000   79.0000   26.1100  915.0000  900.0000
   30.0000   83.0000   28.3300  914.0000  960.0000
   31.0000   79.0000   26.1100  890.0000  970.0000
   32.0000   81.0000   27.2200  912.0000  930.0000
   33.0000   65.0000   18.3300  717.0000  610.0000
   34.0000   67.0000   19.4400  714.0000  620.0000
   35.0000   65.0000   18.3300  724.0000  670.0000
   36.0000   67.0000   19.4400  726.0000  680.0000
   37.0000   66.0000   18.8900  723.0000  650.0000
   38.0000   64.0000   17.7800  726.0000  660.0000
   39.0000   65.0000   18.3300  708.0000  640.0000
   40.0000   80.0000   26.6700  712.0000  900.0000
   41.0000   83.0000   28.3300  917.0000  910.0000
   42.0000   79.0000   26.1100  905.0000  920.0000
   43.0000   82.0000   27.7800  893.0000  980.0000
   44.0000   80.0000   26.6700  920.0000  940.0000
   45.0000   81.0000   27.2200  913.0000  950.0000
   46.0000   81.0000   27.2200  905.0000  940.0000
   47.0000   82.0000   27.7800  896.0000  970.0000
   48.0000   80.0000   26.6700  898.0000  980.0000];

moema code:
% Main script to run MOEMA optimization
clc; 
clear; 
close all;
% Set problem parameters
NPop = 100;  % Population size
T = 200;     % Number of iterations
dim = 30;    % Number of decision variables
lb = zeros(1, dim);  % Lower bound
ub = ones(1, dim);   % Upper bound

% Run MOEMA
[paretoFront, paretoSet, Trajectories, fitness_history, position_history] = MOEMA(NPop, T, lb, ub, dim, @zdt1);

% Plot the Pareto front
figure;
plot(paretoFront(:,1), paretoFront(:,2), 'ro');
xlabel('f1');
ylabel('f2');
title('Pareto Front for ZDT1 using MOEMA');

% Calculate and display metrics
disp(['Number of solutions in Pareto Front: ', num2str(size(paretoFront, 1))]);
disp(['Minimum f1: ', num2str(min(paretoFront(:,1)))]);
disp(['Minimum f2: ', num2str(min(paretoFront(:,2)))]);


function [paretoFront, paretoSet, Trajectories, fitness_history, position_history] = MOEMA(NPop, T, lb, ub, dim, FITNESSFCN)
% Multi-Objective Evolutionary Mating Algorithm (MOEMA)
% MOEMA attempts to solve multi-objective problems of the following form:
%  min F(X) = [f1(X), f2(X), ..., fm(X)]  subject to: lb <= X <= ub
%                  
%  FITNESSFCN   - function handle of the multi-objective fitness function
%  lb           - lower bounds on X
%  ub           - upper bounds on X
%  T            - number of iterations
%  NPop         - size of the population (class size)  
%  paretoFront  - set of non-dominated solutions in objective space
%  paretoSet    - set of non-dominated solutions in decision space
%  Trajectories - best solution trajectory over iterations
%  fitness_history - history of fitness values
%  position_history - history of population positions

% Preallocation
fitness_history = zeros(NPop, T, 2);  % Assuming 2 objectives
position_history = zeros(NPop, T, dim);
Trajectories = zeros(dim, T);

% Generation of initial population
pop = initialization(NPop, dim, ub, lb);
pop_dad = pop(1:NPop/2, :);  % Eqn (1) in the paper
pop_mum = pop(NPop/2+1:end, :); % Eqn (2) in the paper

% Evaluation of objective function for initialization
obj_dad = evaluate_population(pop_dad, FITNESSFCN);
obj_mum = evaluate_population(pop_mum, FITNESSFCN);
obj = [obj_dad; obj_mum];

% Initialize archive with non-dominated solutions
archive = update_archive([], [], pop, obj);

for gen = 1:T  % Start iteration
    k1 = randperm(NPop/2);
    k2 = randperm(NPop/2);
    k1x = pop_dad(k1, :);
    k2x = pop_mum(k2, :);
    
    for k = 1:NPop/2
        % Mating process
        Imates = 1 + (var(k1x(k, :)) - var(k2x(k, :))); % Eqn (4) in the paper
        p = randn(1, dim);  % Eqn (6) in the paper
        r = rand();
        
        if Imates >= 0  % Upper part of Eqn (5) in the paper
            temp1 = p .* k1x(k, :);
            temp2 = (1-p) .* k2x(k, :);
            pop_child = temp1 + temp2;
        else  % Lower part of Eqn (5) in the paper
            temp1 = p .* k2x(k, :);
            temp2 = (1-p) .* k1x(k, :);
            pop_child = temp1 + temp2;
        end
        
        % Impact of best environment (using random non-dominated solution)
        if ~isempty(archive.pop)
            best_pop = archive.pop(randi(size(archive.pop, 1)), :);
            K = rand(1, dim) < 0.8;  % Eqn (8) in the paper
            pop_child = best_pop .* (1-K) + pop_child .* K;  % Eqn (7) in the paper
        end
        
        % Bounding of the solution
        pop_child = max(min(ub, pop_child), lb);
        
        % Evaluate child
        obj_child = FITNESSFCN(pop_child);
        
        % Update population using non-dominated sorting
        [pop_dad, obj_dad] = update_population(pop_dad, obj_dad, pop_child, obj_child, k1(k));
        [pop_mum, obj_mum] = update_population(pop_mum, obj_mum, pop_child, obj_child, k2(k));
        
        % Exploration process
        if r < 0.2
            if ~isempty(archive.pop)
                temp1 = archive.pop(randi(size(archive.pop, 1)), :);
                pop_child = rand(1, dim) .* temp1;  % Eqn (9) in the paper
                pop_child = max(min(ub, pop_child), lb);
                obj_child = FITNESSFCN(pop_child);
                
                % Update population using non-dominated sorting
                [pop_dad, obj_dad] = update_population(pop_dad, obj_dad, pop_child, obj_child, randi(NPop/2));
                [pop_mum, obj_mum] = update_population(pop_mum, obj_mum, pop_child, obj_child, randi(NPop/2));
            end
        end
    end
    
    % Combine updated pool dad and mum
    pop = [pop_dad; pop_mum];
    obj = [obj_dad; obj_mum];
    
    % Update archive
    archive = update_archive(archive.pop, archive.obj, pop, obj);
    
    % Record the history at each iteration
    fitness_history(:, gen, :) = obj;
    position_history(:, gen, :) = pop;
    
    % Update Trajectories (using a random non-dominated solution)
    if ~isempty(archive.pop)
        Trajectories(:, gen) = archive.pop(randi(size(archive.pop, 1)), :)';
    end
    
    disp(['Iteration #', num2str(gen), ' completed. Archive size: ', num2str(size(archive.pop, 1))]);
end

% Return Pareto front and Pareto set
paretoFront = archive.obj;
paretoSet = archive.pop;

end

% Helper Functions

function pop = initialization(NPop, dim, ub, lb)
    pop = rand(NPop, dim) .* (ub - lb) + lb;
end

function obj = evaluate_population(pop, FITNESSFCN)
    obj = zeros(size(pop, 1), 2);  % Assuming 2 objectives
    for i = 1:size(pop, 1)
        obj(i, :) = FITNESSFCN(pop(i, :));
    end
end

function [pop, obj] = update_population(pop, obj, child, child_obj, index)
    if dominates(child_obj, obj(index, :))
        pop(index, :) = child;
        obj(index, :) = child_obj;
    elseif ~dominates(obj(index, :), child_obj)
        if rand() < 0.5
            pop(index, :) = child;
            obj(index, :) = child_obj;
        end
    end
end

function archive = update_archive(archive_pop, archive_obj, pop, obj)
    combined_pop = [archive_pop; pop];
    combined_obj = [archive_obj; obj];
    
    [is_dominated, ~] = pareto_dominance(combined_obj);
    non_dominated = ~is_dominated;
    
    archive.pop = combined_pop(non_dominated, :);
    archive.obj = combined_obj(non_dominated, :);
end

function [is_dominated, domination_count] = pareto_dominance(objectives)
    [n_solutions, n_objectives] = size(objectives);
    is_dominated = false(n_solutions, 1);
    domination_count = zeros(n_solutions, 1);

    for i = 1:n_solutions
        for j = i+1:n_solutions
            if dominates(objectives(i, :), objectives(j, :))
                is_dominated(j) = true;
                domination_count(i) = domination_count(i) + 1;
            elseif dominates(objectives(j, :), objectives(i, :))
                is_dominated(i) = true;
                domination_count(j) = domination_count(j) + 1;
            end
        end
    end
end

function result = dominates(obj1, obj2)
    result = all(obj1 <= obj2) && any(obj1 < obj2);
end

function f = zdt1(x)
    f1 = x(1);
    g = 1 + 9 * sum(x(2:end)) / (length(x) - 1);
    h = 1 - sqrt(f1 / g);
    f2 = g * h;
    f = [f1, f2];
end