I have a similar problem.Although my problem is multi-objective, I need to reach a definite result in the end not an array of compromised results as Pareto suggests.I have used fuzzy decision making to satisfy this need but I don't know how I can justify the solution is the best compromised one.For this reason I thought about aggregating objectives or using e-constraints methods which considers one main objective and the rest are applied as constraints.Since my programming skill is limited I have been unable to implement it in this code.Do you have any idea how can I do it?
Dear Song Lin,
Is there anyway t hat I can apply constraints on variables that are not part of design variables?
For example one of my Objective Functions is Pl and this is a function of x which is my design variable.The thing is that Pl also depends on another variable(here named as V) which is not in design variable .
Is it possible to do so?
I am quite new to the concept but the thing that made me to work on your file is looking for a Decision Making way of Pareto results.My question is in regard with Objectives and variables.Do we need to define Objectives and design variables for this program or the only required input of this program is results produced in my optimization?
Thanks for the great work!!!
You have explained about constraints (Your manual Page 1)
But if constraints are like say,
Then how to modify the code in objective function (Page 3)
Thanks and regards,
Great work, thanks a lot for sharing!
In the first few generations, some individuals of the population have violated constraints. I was expecting to find the actual contraints variables in results.pops.cons, but there were all zero. I only found the *number* of violations in results.pops.nViol.
I was able to improve this by adding one line in evaluate.m:
% Save the objective values and constraint violations
indi.obj = y; % <<<< ADDED LINE >>>
if( ~isempty(indi.cons) )
indi.cons = cons;
idx = find( cons );