binary multiobjective optimization with genetic algorism

Hi,

I have tried to write a matlab code to solve the binary multiobjective optimization problem using the genetic algorithm. I am using the matlab built-in function "gamultobj", expecting to obtain the nultiple pareto points with the values of decision variables of {0,1}. I will very appreciate if you let me know the tips for coding it. Thanks.

Answer 1

[Tushar Behera]

Hi Cheol-Joo Cho,

I believe you want to implement genetic algorithm to solve a multi objective optimization problem where the decision variables are binary using MATLAB.

You can run the below command in command window to see an example using “gamultiobj” function.

>>openExample('globaloptim/gamultiobjoptionsdemo') 

Also, I found a MATLAB answer link which I believe can give you more information.

Is it possible to solve a mixed-integer multi-objective optimization problem using Global Optimization Toolbox? - MATLAB Answers - MATLAB Central (mathworks.com)

Apart from that you can also use “ga” which is an inbuilt function in Optimization Toolbox to implement genetic algorithm to solve your multi-objective problem.

>>[x,fval,exitflag] = ga(fitnessfcn,nvars,A,b,[],[],lb,ub,nonlcon,IntCon,options) 

Here Intcon will help you to set your decision variables as integers. IntCon is a vector of positive integers that contains the x components that are integer valued. For example, if you want to restrict x(2) and x(10) to be integers, set IntCon to [2,10].

To set the binary constraints in “ga", you can set the lower and upper bounds to 0 and 1 respectively.

Here is the link for additional documentation:

http://www.mathworks.com/help/gads/mixed-integer-optimization.html

Thanks, and regards,

Tushar Behera