Here's an example of MATLAB code for solving optimization problems using the CEC 2017 single-objective real-parameter functions with bound constraints. The code assumes that you have the function definitions and the necessary input data.

% CEC 2017 Single Objective Real Parameters Functions with Bound Constraints

% MATLAB Code

% Add the folder containing the functions to the MATLAB path

addpath('cec17');

% Set the function number and dimensionality

functionNumber = 1; % Change this to the desired function number

dimensionality = 10; % Change this to the desired dimensionality

% Load the function definitions and the input data

load(sprintf('cec17/input_data/shift_data_%02d.mat', functionNumber));

% Define the lower and upper bounds for the optimization variables

lowerBounds = -100 * ones(1, dimensionality);

upperBounds = 100 * ones(1, dimensionality);

% Set the maximum number of function evaluations

maxFuncEvals = 10000 * dimensionality;

% Initialize the random number generator with a seed

rng('default');

% Generate a random initial solution within the specified bounds

initialSolution = lowerBounds + rand(1, dimensionality) .* (upperBounds - lowerBounds);

% Define the objective function

objectiveFunction = @(x) cec17_func(x, functionNumber, shift_data, dimensionality);

% Define the options for the optimization algorithm

options = optimoptions('fmincon', 'Display', 'iter', 'MaxFunctionEvaluations', maxFuncEvals);

% Perform the optimization using the fmincon function

[optimalSolution, optimalValue] = fmincon(objectiveFunction, initialSolution, [], [], [], [], lowerBounds, upperBounds, [], options);

% Display the optimal solution and the optimal value

fprintf('Optimal Solution: ');

disp(optimalSolution);

fprintf('Optimal Value: %e\n', optimalValue);

Please note that this code assumes that you have the CEC 2017 benchmark functions and the input data files saved in a folder named cec17 in your MATLAB working directory. You can obtain these files from the CEC 2017 competition website or other reliable sources.

Remember to update the functionNumber and dimensionality variables according to the specific problem you want to solve. Additionally, you may need to adjust the lowerBounds and upperBounds variables to match the desired bound constraints for your optimization problem.

Make sure to have the Optimization Toolbox installed in your MATLAB environment to use the fmincon function for constrained optimization.

Another example of MATLAB code :

% CEC 2017 Single Objective Real Parameters Functions with Bound Constraints

% MATLAB Code

% Add the folder containing the functions to the MATLAB path

addpath('cec17');

% Set the function number and dimensionality

functionNumber = 5; % Change this to the desired function number

dimensionality = 30; % Change this to the desired dimensionality

% Load the function definitions and the input data

load(sprintf('cec17/input_data/shift_data_%02d.mat', functionNumber));

% Define the lower and upper bounds for the optimization variables

lowerBounds = -100 * ones(1, dimensionality);

upperBounds = 100 * ones(1, dimensionality);

% Set the maximum number of function evaluations

maxFuncEvals = 10000 * dimensionality;

% Initialize the random number generator with a seed

rng('default');

% Generate a random initial solution within the specified bounds

initialSolution = lowerBounds + rand(1, dimensionality) .* (upperBounds - lowerBounds);

% Define the objective function

objectiveFunction = @(x) cec17_func(x, functionNumber, shift_data, dimensionality);

% Define the options for the optimization algorithm

options = optimoptions('ga', 'Display', 'iter', 'MaxGenerations', maxFuncEvals/dimensionality, 'UseVectorized', true);

% Perform the optimization using the ga function

[optimalSolution, optimalValue] = ga(objectiveFunction, dimensionality, [], [], [], [], lowerBounds, upperBounds, [], options);

% Display the optimal solution and the optimal value

fprintf('Optimal Solution: ');

disp(optimalSolution);

fprintf('Optimal Value: %e\n', optimalValue);

In this example, we use the Genetic Algorithm (GA) optimization method (ga function) from MATLAB's Global Optimization Toolbox. This method does not require the Optimization Toolbox's fmincon function used in the previous example.

Again, please make sure to have the CEC 2017 benchmark functions and the input data files saved in a folder named cec17 in your MATLAB working directory. Adjust the functionNumber and dimensionality variables according to your specific problem, and update the lowerBounds and upperBounds variables to match the desired bound constraints.

Remember to have the Global Optimization Toolbox installed in your MATLAB environment to use the ga function.

Feel free to modify and adapt the code to suit your needs. I hope this second example helps you in solving CEC 2017 single-objective real-parameter functions with bound constraints using MATLAB!