Hi,
I understand that you're trying to solve a constrained minimization problem using the "ga" function from MATLAB's Global Optimization Toolbox, but you're encountering an early termination with a message indicating small changes in fitness and constraint violation—even though you've tightened tolerances.
I assume that your goal is to get the GA algorithm to properly handle your nonlinear constraints and not exit prematurely due to perceived convergence.
In order to ensure GA correctly solves this constrained optimization problem, you can follow the below steps:
Step 1: Use GA-specific options with "gaoptimset"
You're currently using "optimset", which is intended for functions like fmincon. For GA, use "gaoptimset" to specify GA-relevant options like "TolFun" and "TolCon".
Step 2: Increase population size or generations
GA may terminate early if the population is too small to explore the constraint landscape well. Increase the population size and the number of generations to allow more exploration.
Step 3: Verify constraints are not too restrictive
Ensure that the constraint function is not overly limiting feasible regions. Use plotting or manual checks to see if feasible regions exist within your bounds.
Step 4: Add plot functions to monitor progress
Use the "PlotFcn" option to add diagnostic plots. This can help visualize how GA progresses with constraints.
Step 5: Ensure constraints and objective are continuous and differentiable
Though GA does not rely on gradients, discontinuous functions may still lead to early convergence or poor feasibility.
Here’s a quick summary of revised option settings:
- Use:
options = gaoptimset('TolFun',1e-8,'TolCon',1e-8,'PopulationSize',100,'Generations',200,'PlotFcn',{@gaplotbestf,@gaplotmaxconstr});
Refer to the documentation of the functions used:
- "ga": https://www.mathworks.com/help/gads/ga.html
- "gaoptimset": https://www.mathworks.com/help/gads/gaoptimset.html
Hope this helps!
