Comments (2)
from simanneal.
Thank you for your reply,
I am writing a paper on solving the Quadratic Knapsack Problem with different methods, one of them being SA. I will forward it to you once it is hopefully published.
The way I implemented the move()
method was:
- try to add a random item among those which would not violate the capacity constraint.
- If no item adding is possible without violation the constraint, try to swap 2 random items until I find a pair which would satisfy the constraint.
To make the move "fast" I use np.dot()
to evaluate in parallel capacity and profit of each variable if it were removed/added.
I haven't finished benchmarking but I setup Tmax
to the maximum worse ΔE possible, e.g the maximum row(column) sum value between each variable in the symmetrized profit matrix, divided by 10. I repeat short burst of SA with steps
between 500 and 1500 depending on the instance.
from simanneal.
Related Issues (20)
- .auto() yields tmax == tmin, and negative remaining time HOT 2
- Problem in code HOT 1
- copy_state() returns None HOT 1
- Allow any type of state variable HOT 1
- multithreading HOT 2
- updates not printed out HOT 4
- Is it possible to perform a continuous problem with this package?
- Dependency injection for external data
- Travis builds failing HOT 1
- tsp example move() needs a fix
- question on multi-objective HOT 1
- Unit tests HOT 1
- ZeroDivisionError in anneal.py HOT 4
- Temperature and Energy stuck at zero HOT 1
- Litterature reference for simulated annealing HOT 2
- Pass current temperature to move function HOT 2
- Annealing problem HOT 1
- Speeding Up the Calculation HOT 2
- Replacing lines to shorten output HOT 2
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from simanneal.