Hi Yahya,
I understand you want to find the optimal value for the input parameters to get the maximum value for the output variables using the genetic algorithm.
You can find the optimal value of more than one variable using multi-objective optimization. The multi-objective optimization problems are solved by creating an objective function for the output variables using the linear combinations of the input parameters and then trying to find the best possible value for the objective function by varying the input parameters.
To use the genetic algorithm for the same, you need to create the objective functions for your output variables using the linear combination of the input parameters, and then using the "gamultiobj" function, you can get the optimal value for the input parameters.
Please refer to the below example for the reference.
% Define the objective function
% x(1): Temperature
% x(2): Volume of AL203
objective = @(x) objectiveFunction(x(1), x(2));
% Define the number of input parameters and their bounds
nvars = 2; % Number of decision variables (Temperature and Volume of ALO203)
lb = [0, 0]; % Lower bounds for the input parameters
ub = [100, 100]; % Upper bounds for the input parameters
% Run the genetic algorithm
[x, fval] = gamultiobj(objective, nvars, [], [], [], [], lb, ub);
% Display the optimal solutions
disp('Optimal solutions:');
disp(x);
disp('Objective function values:');
disp(fval);
Please refer to the following documentation links to know more about the multi-objective optimization and how to perform multi-objective optimization using the genetic algorithm:
Hope this helps!
Regards,
Aman Mehta
