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)
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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- Mathematics and Optimization > Global Optimization Toolbox > Genetic Algorithm >
- Mathematics and Optimization > Global Optimization Toolbox > Multiobjective Optimization >
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