Simulated Annealing using tqdm
Project description
Simulated Annealing package for Python, using tqdm
Installation
pip install frigidum
Basic Example Usage
import frigidum
import random
def random_start():
return 50 + random.random()
def random_small_step(x):
return x + 0.1 * (random.random() - .5)
def random_big_step(x):
return x + 10 * (random.random() - .5)
def obj(x):
return x**2
local_opt = frigidum.sa(random_start=random_start,
neighbours=[random_small_step, random_big_step],
objective_function=obj,
T_start=100,
T_stop=0.000001,
repeats=10**4,
copy_state=frigidum.annealing.naked)
Arguments:
random_start
: function which returns a random start / state.objective_function
: objective function to minimize.neighbours
: list of neighbour functions, for one use [neighbour]. For each proposal, a neighbour is randomly selected (equal weights).T_start
: Starting temperature.T_stop
: Stopping temperature.alpha
: Lower temperature by this factor, after repeats proposals.repeats
: at each temperature lowering by factoralpha
, do repeats proposals.copy
=frigidum.annealing.copy
,frigidum.annealing.deepcopy
,frigidum.annealing.naked
, or custom - the copy method.
Output & Return
- During a run,
print
TemperateT
, Movements proportion in current batch of repeatsM
, minimum Objective found so farO_min
, last-accepted Objective valueO_current
, and various progress information provided bytqdm
. - At the end,
print
movement statistics of neighbours used. - returns a
(best_found_state, objective_function(best_found_state) )
tuple when done.
Movements
A movement is a when a proposed state is accepted, and the objective function has changed. For each batch of repeats, the proportion of movements are displayed. The number of movements differ with the number of accepted proposals, as a proposal might not necessary change the objective function value.
-
In the early phase of annealing, movements should happen >90%.
-
In the last phase of annealing, movements should happen <10%.
Movements are useful to determine the starting- and stopping temperature; T_start
& T_stop
, with the above guidelines.
Copy'ing of States
3 most important copy methods are included in the annealing
module,
def copy(state):
return state.copy()
def deepcopy(state):
return state.deepcopy()
def naked(state):
return state
In the example, naked
with the argument copy_state=frigidum.annealing.naked
is used,
- use
copy_state=frigidum.annealing.copy
forcopy()
, - use
copy_state=frigidum.annealing.deepcopy
fordeepcopy()
, - use
copy_state=frigidum.annealing.naked
ifa = b
would already create a copy, or if the neighbour function return copies.
General Advice with Simulated Annealing
- Use the movements statistics to set the starting and stopping temperature.
- Focus on the neighbour function, not the cooling scheme or acceptance variations.
- To get inspiration for random neighbours, try solve a similar problem yourself.
- Try multiple neighbours together, combinations usually work well. The
neighbours
argument expects a list of neighbours. After a run, statistics are displayed. Don't discard a neighbour just by its statistics, it might be a catalyst for a different neighbours. - Try add neighbours, that might work well when cold.
- Try add neighbours, that might work well when warm.
- Try add neighbours, that find a local minima with local greedy algorithm.
- Try add neighbours, that break/remove a local solution and fix it again.
- Try add neighbours, that overwrite a part of the solution rigorously.
- It is difficult to predict the effect of a random neighbour, ideas usually don't survive the outcome of experiments.- When conditions apply, stay within the feasible zone when possible. -or-
- Only anneal on either condition or objective, not both at the same time.
Examples
Rastrigin Function
https://en.wikipedia.org/wiki/Rastrigin_function
from frigidum.examples import rastrigin
frigidum.sa(random_start=rastrigin.random_start,
objective_function=rastrigin.rastrigin_function,
neighbours=[rastrigin.random_small_step, rastrigin.random_big_step],
copy_state=frigidum.annealing.naked,
T_start=100,
T_stop=0.00001,
repeats=10**4)
To-Do:
- Add TSP as example
- Multi threading (N simultaneous anneals)
- Drilling (after repeats, re-repeat with low temp)
- Re-Annealing.
- Re-Annealing with
N
challengers. - Temperature dependent proposals.
- Stopping criterea for objective, i.e. stop when objective value is below certain threshold (often 0).
- (?) Auto-set start Temperature (Based on >90% movements)
- (?) Auto-stop (Based on <10% movements)
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