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?
Can someone help how to write a binary constraint, in particular a variable to take 1 or 0 value. I am working on a facility location problem and I need YES/NO decision constraints. One idea that I got (not working) was putting the boundaries of the variables 0 and 1, and plus writing a constraint like this:
cons(22) = x(2) - 1;
cons(23) = x(1) - 1;
I want the function to choose between the variable x(1) or x(2)
Iman or Ivan, thanks for your response. In the code I have tried the following but still cant get a maximum optimization (still negative values).
options.objfun = @(-NSGA2_Objfun);
*doesn't work still looking up @ function properties in matlab.
Or within the NSGA2_Objfun I multiple the final result by negative one.
Dear Song Lin,
Thanks for your great work. I am researching on cognitive radios and use NPGM with integer coding. I have an binary assignment matrix and a cost matrix. Each population member represents an assignment matrix and the objective evaluated based on cost.
Each solution is a vector of size 90 with integer values in it. Even though, I set the 'options.vartype=2' for integer optimization, at the output result, I have seen real values in population member. How can I fix this issue? Thanks for your great effort and help.