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ACO-Pants-0.5.2
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مشاهده بیشترتوضیحات
A Python3 implementation of the ACO Meta-Heuristic
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ACO-Pants-0.5.2 |
| نام | ACO-Pants |
| نسخه کتابخانه | 0.5.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Robert Grant |
| ایمیل نویسنده | rhgrant10@gmail.com |
| آدرس صفحه اصلی | http://pypi.python.org/pypi/ACO-Pants |
| آدرس اینترنتی | https://pypi.org/project/ACO-Pants/ |
| مجوز | LICENSE.txt |
=====
Pants
=====
A Python3 implementation of the Ant Colony Optimization Meta-Heuristic
--------
Overview
--------
**Pants** provides you with the ability to quickly determine how to
visit a collection of interconnected nodes such that the work done is
minimized. Nodes can be any arbitrary collection of data while the edges
represent the amount of "work" required to travel between two nodes.
Thus, **Pants** is a tool for solving traveling salesman problems.
The world is built from a list of nodes and a function responsible for
returning the length of the edge between any two given nodes. The length
function need not return actual length. Instead, "length" refers to that
the amount of "work" involved in moving from the first node to the second
node - whatever that "work" may be. For a silly, random example, it could
even be the number of dishes one must wash before moving to the next
station at a least dish-washing dish washer competition.
Solutions are found through an iterative process. In each iteration,
several ants are allowed to find a solution that "visits" every node of
the world. The amount of pheromone on each edge is updated according to
the length of the solutions in which it was used. The ant that traveled the
least distance is considered to be the local best solution. If the local
solution has a shorter distance than the best from any previous
iteration, it then becomes the global best solution. The elite ant(s)
then deposit their pheromone along the path of the global best solution
to strengthen it further, and the process repeats.
You can read more about `Ant Colony Optimization on
Wikipedia <http://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms>`_.
------------
Installation
------------
Installation via ``pip``
.. code-block:: console
$ pip3 install ACO-Pants
-----
Usage
-----
Using **Pants** is simple. The example here uses Euclidean distance
between 2D nodes with ``(x, y)`` coordinates, but there are no real
requirements for node data of any sort.
1) Import **Pants** (along with any other packages you'll need).
.. code-block:: python
import pants
import math
import random
2) Create your data points; these become the nodes. Here we create some
random 2D points. The only requirement for a node is that it is
distinguishable from all of the other nodes.
.. code-block:: python
nodes = []
for _ in range(20):
x = random.uniform(-10, 10)
y = random.uniform(-10, 10)
nodes.append((x, y))
3) Define your length function. This function must accept two nodes and
return the amount of "work" between them. In this case, Euclidean
distance works well.
.. code-block:: python
def euclidean(a, b):
return math.sqrt(pow(a[1] - b[1], 2) + pow(a[0] - b[0], 2))
4) Create the ``World`` from the nodes and the length function.
.. code-block:: python
world = pants.World(nodes, euclidean)
5) Create the ``Solver``.
.. code-block:: python
solver = pants.Solver()
6) Solve the ``World`` with the ``Solver``. Two methods are provided for
finding solutions: ``solve()`` and ``solutions()``. The former
returns the best solution found, whereas the latter returns each
solution found if it is the best thus far.
.. code-block:: python
solution = solver.solve(world)
# or
solutions = solver.solutions(world)
7) Inspect the solution(s).
.. code-block:: python
print(solution.distance)
print(solution.tour) # Nodes visited in order
print(solution.path) # Edges taken in order
# or
best = float("inf")
for solution in solutions:
assert solution.distance < best
best = solution.distance
Run the Demo
------------
Included is a 33 "city" demo script that can be run from the command line.
.. code-block:: console
user@host:~$ pants-demo -h
usage: pants-demo [-h] [-V] [-a A] [-b B] [-l L] [-p P] [-e E] [-q Q] [-t T]
[-c N] [-d D]
Script th;at demos the ACO-Pants package.
optional arguments:
-h, --help show this help message and exit
-V, --version show program's version number and exit
-a A, --alpha A relative importance placed on pheromones; default=1
-b B, --beta B relative importance placed on distances; default=3
-l L, --limit L number of iterations to perform; default=100
-p P, --rho P ratio of evaporated pheromone (0 <= P <= 1); default=0.8
-e E, --elite E ratio of elite ant's pheromone; default=0.5
-q Q, --Q Q total pheromone capacity of each ant (Q > 0); default=1
-t T, --t0 T initial amount of pheromone on every edge (T > 0);
default=0.01
-c N, --count N number of ants used in each iteration (N > 0); default=10
-d D, --dataset D specify a particular set of demo data; default=33
For best results:
* 0.5 <= A <= 1
* 1.0 <= B <= 5
* A < B
* L >= 2000
* N > 1
For more information, please visit https://github.com/rhgrant10/Pants.
user@host:~$ pants-demo
Solver settings:
limit=100
rho=0.8, Q=1
alpha=1, beta=3
elite=0.5
Time Elapsed Distance
--------------------------------------------------
0:00:00.017490 0.7981182992833705
0:00:00.034784 0.738147755518648
0:00:00.069041 0.694362159048816
0:00:00.276027 0.6818083968312925
0:00:00.379039 0.6669398280432167
0:00:00.465924 0.6463548571712562
0:00:00.585685 0.6416519698864324
0:00:01.563389 0.6349308484274142
--------------------------------------------------
Best solution:
0 = (34.02115, -84.267249)
9 = (34.048194, -84.262126)
6 = (34.044915, -84.255772)
22 = (34.061518, -84.243566)
23 = (34.062461, -84.240155)
18 = (34.060461, -84.237402)
17 = (34.060164, -84.242514)
12 = (34.04951, -84.226327)
11 = (34.048679, -84.224917)
8 = (34.046006, -84.225258)
7 = (34.045483, -84.221723)
13 = (34.051529, -84.218865)
14 = (34.055487, -84.217882)
16 = (34.059412, -84.216757)
25 = (34.066471, -84.217717)
24 = (34.064489, -84.22506)
20 = (34.063814, -84.225499)
10 = (34.048312, -84.208885)
15 = (34.056326, -84.20058)
5 = (34.024302, -84.16382)
32 = (34.118162, -84.163304)
31 = (34.116852, -84.163971)
30 = (34.109645, -84.177031)
29 = (34.10584, -84.21667)
28 = (34.071628, -84.265784)
27 = (34.068647, -84.283569)
26 = (34.068455, -84.283782)
19 = (34.061281, -84.334798)
21 = (34.061468, -84.33483)
2 = (34.022585, -84.36215)
3 = (34.022718, -84.361903)
4 = (34.023101, -84.36298)
1 = (34.021342, -84.363437)
Solution length: 0.6349308484274142
Found at 0:00:01.563389 out of 0:00:01.698616 seconds.
user@host:~$
Known Bugs
----------
None of which I am currently aware. Please let me know if you find
otherwise.
Troubleshooting
---------------
Credits
-------
- Robert Grant rhgrant10@gmail.com
License
-------
GPL
نحوه نصب
نصب پکیج whl ACO-Pants-0.5.2:
pip install ACO-Pants-0.5.2.whl
نصب پکیج tar.gz ACO-Pants-0.5.2:
pip install ACO-Pants-0.5.2.tar.gz