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Constrained MOO using GA (ver. 2)

version 1.5 (2.05 KB) by Sam Elshamy
Solving a simple MOO problem using Genetic Algorithms (GA)

6 Downloads

Updated 30 Nov 2014

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This code is a demo of using Genetic Algorithms (GA) to solve a simple constrained multi-objective optimization (MOO) problem.
The objective is to find the pareto front of the MOO problem defined as follows:
Maximize:
f1(X) = 2*x1 + 3*x2
f2(X) = 2/x1 + 1/x2
such that:
10 > x1 > 20
20 > x2 > 30

The set of non-dominated solutions is plotted in the objective space, and displayed in the console.

Cite As

Sam Elshamy (2020). Constrained MOO using GA (ver. 2) (https://www.mathworks.com/matlabcentral/fileexchange/29806-constrained-moo-using-ga-ver-2), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (9)

any examples of source code on multiobjective optimization with mix integer variables?I have 3 objective functions, 6 decision variables....thanks very much

Sam Elshamy

@Yu-Yun. You are right. I fixed this bug and other bugs I found in my code and uploaded a revised version.

Thank you.

Yu-Yun

I think line 68 and 69 are supposed to be
pool(i,3)=2*pool(i,1)*2+3*pool(i,2);
pool(i,4)=2/pool(i,1)*2+1/pool(i,2);

Updates

1.5

Now available in Toolbox format.

1.4.0.0

Update: Bugs in line 68 and 69 and others are now fixed. Thanks to Yu-Yun

MATLAB Release Compatibility
Created with R2009a
Compatible with any release
Platform Compatibility
Windows macOS Linux