Binary solutions with GA
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I am currently working with GA on resource allocation and the goal is to find binary solutions with a non linear objective and linear constraints.
At the beginning of the project, I had a problem with more than 700 variables with many constraints and the solutions that I had from GA were very irrelevant. I thought the size of the problem was the issue.
So I broke down the problem into many models in order to have less that hundreds variables (in occurrence 30 variables for the current model I am building) and around hundreds constraints too for each model (in occurrence 95 linear constraints for the same current model). Besides, some of the constraints are normally equality constraints that I transformed into inequality constraints to help GA to find a solution. BUT at the end I still have solutions that don't correspond to the constraints. For the instance, for example if we want to allocate 30 bags in 5 cabinets so that each bag is allocated only in one cabinet, I got some results where one bag is allocated in many cabinets, even though the inequality constraints that I wrote seem totally correct.
I have this report at the end "Optimization terminated: average change in the penalty fitness value less than options.FunctionTolerance and constraint violation is less than options.ConstraintTolerance".
I don't really know what to do with this problem and I am quite in a need of a solution for an industrial (confidential) problem after having purchased GA weeks ago (the trials for smaller sizes seemed to work previously). Please could you give any support for that?
回答 (1 件)
Brendan Hamm 2017 年 9 月 20 日
This will be a bit length to answer this question again, but I have provided a way you can solve this problem in a previous post:
This does use a multiobjective problem, but the idea of writing the population, crossover and mutation functions is the same.