I have a problem with two objectives. The problem is minimized by NSGA-2 algorithm. I run minimize with different population sizes and different termination criterias, but every time the algorithm just gives only two solutions. I know from logs that there are some other proper solutions but I can't see it in pareto-front. How can I get more solutions? Do you have an idea?
Thank you...
I tried to increase population size, number of generations, termination criteria. I tried to change objective function. I tried to apply different mutation, selection and crossover functions. However, all of these resulted in just two solutions. I want to get more solutions, I know there are more solutions but the algorithm just gives me two.
To have an idea whether your problem has converged or not, you can plot the hypervolume. It shows how much your pareto-front is moving in function of number of evaluations. Documentation is here:
https://pymoo.org/getting_started/part_4.html?highlight=hypervolume
Secondly, are you sure your known solution is not being scrapped by any constraints? Is the range of design variables set so that they can find your optimum?