Constrained MOO using GA (ver. 2)

版本 1.5 (2.0 KB) 作者: Sam Elshamy
Solving a simple MOO problem using Genetic Algorithms (GA)
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更新时间 2014/11/30

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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.

引用格式

Sam Elshamy (2024). Constrained MOO using GA (ver. 2) (https://www.mathworks.com/matlabcentral/fileexchange/29806-constrained-moo-using-ga-ver-2), MATLAB Central File Exchange. 检索来源 .

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版本 已发布 发行说明
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
1.0.0.0