Simulated annealing for optimization
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Hello everyone,
i have a question to simulated annealing. I want to use it to solve a quadratic asignment problem (QAP) because it is very large and it takes to long to solve it with an exact algorithm. Is it possible to solve a QAP with simulated annealing ?
And how can I solve it with constraints like or . I can just give the algorithm a start value and a lower bound / upper bound.
Can please someone help me ?
Best regards !
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Walter Roberson
on 22 May 2019
Generally speaking, simulated annealing can be used to solve QAP, but it would likely be much much slower than a routine designed for solving QAP.
simulannealbnd supports bounds constraints, but no other kinds of constraints, and it does not support anything like event functions that might provide constraints. You would therefore need to code the constraints as a penalty, which is always risky because the routines do not know to avoid penalty areas.
Simulated Annealing is a slow process. You would probably be better off with patternsearch()
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Walter Roberson
on 23 May 2019
I already told you that it is not possible to set constraints with simmulannealbnd . All you can do is add a penalty when the constraint is violated. However when you use penalties, you have to plan them carefully. For example if you return 1000000 when the constraint is violated, then SA would be happy to try to search for a location that returned only 999999.99999 instead of understanding that the area is to be denied.
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