SwarmPy

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SwarmPy is an experimental library.
It aims at providing a modulable framework to test and experiment on Ant Colony Optimzation (ACO) algorithms on Travelling Salesman Problem
contact : lucas.saban[at]ensae.fr

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⚡️ Quick start

First let's import the librairies we will use

import os 
import numpy as np
import matplotlib.pyplot as plt
os.chdir('..')

from swarmpy_tsp import *

The ant_coder function can load the TSP test sets of the test_set folder, namely :

• berlin52
• ch130
• Any test set you wish, as long you add them to folder following the same format.

G is the graph representation of the problem. Its a dictionnary containing a uninitialized pheromone matrix, a heuristics matrix and a cost matrix

G, opt_score = Antcoder('test_set/berlin52')

Ant colony optmization algorithms can be built in the same fashion as a Pipeline object from scikit-learn. You only need to put the blocks in the right order.

Careful ! The G dictionnary is modified inplace during computation. If you want to try different run with the same datapoints, you shall use deepcopy from the copy built-in library.

Below, you can see an example of an ACO Pipeline

aco_pipeline = ACO_Pipeline(
    [
        ("Planner", Planner({"alpha": 1.0, "beta": 2.0})),
        ("Sol", SolutionConstructor()),
        ("DA", DaemonActions()),
        ("Updater", BestTourPheromonesUpdater()),

    ], 
    iter_max=60
)

An ACO_Pipeline is composed by objects inhering from the semi abstract class ACO_Step. Those steps are more or less the main composants of any ACO Algorithms and are independently built so that one can use them in other situations or can easily build new blocks. Let's dive in !

The ACO_Steps

Planner

First of all, there is the Planner step. In very basic use cases, it is not really important and only serves as the place to define your ants parameters. In more advanced cases, such as iteration-dependent ants parameters, that's where the magic happen.

The Planner object have one parameter which is a dict of those parameters :

• alpha: coefficient associated to the pheromone trails
• beta: coefficient associated to the heuristic information
• q: Corresponds to the level of exploitation in Ant Colony System. When not precised, it is set to 0 which is equivalent to Ant System
• mask: Corresponds to a mask applied on the graph. It is useful when you want to reduce your problem to a subset of cities. If not precized, a generic all covering mask is set.

If you are not familliar with those notations please refer to appropriate ACO litterature (Dorigo, Stützle, ...)

The RandomizedPlanner object, which inheritates from the Planner, introduces random sampling of ants parameters. It have two more parameters, alpha_bounds and beta_bounds which are the bounds of the uniform distribution from which each ant parameters is drawn.

To add iteration-dependant ant parameters, I'd recommend to inheritate from Planner ant induce mutations in the run method which takes the iteration number as argument.

An example of instanciation :

To keep a parameter constant with RandomizedPlanner, setting low_bound = high_bound does the job

planner_step = RandomizedPlanner(alpha_bounds=[1.0, 1.0], beta_bounds=[1., 6.], ant_params={'q':0.8})

The SolutionConstructor

The SolutionConstructor step is at the core of ACO. It is where ants are actually going through the graph to build feasible solutions. No parameters are required. For now, ants are running concurrently, using the thread built-in library of Python. Parallel computation should arrive soon. It should be remarked that solution are sorted by descending quality. An example of instanciation :

construction_step = SolutionConstructor()

DaemonActions

If the first steps are kind of necessary, this one can totally be exempted from the pipeline. It implements a 2-opt local search on the solutions built during the construction step. It as one parameter, k, that is the number of best solutions that shall go through local search.

An example of instanciation :

daemon_step = DaemonActions(k=2)

PheromonesUpdaters

It's the final step of an ACO algorithm iteration. It evaporates pheromones and reinforces pheromone trails according the solutions found. General parameters are :

• rho : the evaporation rate (can be set to 0 for no evaporation)
• Q : The normalization parameter of the pheromone reinforcement update (Q/cost)
• bounds : if precised, it implements MMAS policy of bounding pheromone trails

Then there is 3 Updaters that inherits from the abstract base Updater :

• ProportionnalPheromonesUpdater : basic updater, reinforcement in Q/cost for all solutions
• BestSoFarPheromonesUpdater : Reinforces the k best paths found so far
• BestTourPheromonesUpdater: Reinforces the k best paths found in the current lap

Those steps can totally be combined or made iteration-dependant by inheritance.

An example of instanciation :

ph_updater = BestTourPheromonesUpdater(k=2, bounds=[0.1, 1])

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Now you can play with it !

The ACO_Pipeline object return the list of all solutions built during computation

solutions_w_daemon = aco_pipeline.run(G=G)
scores_w_daemon = np.array([el[1] for el in solutions_w_daemon['solutions']]) - opt_score

plt.plot(scores_w_daemon)
plt.xlabel('Iterations')
plt.ylabel('Gap between best solution and optimal solution')
plt.show()

SwarmPy | Score : 7859.448968094198: 100%|██████████| 60/60 [00:13<00:00,  4.41it/s]

For an advanced example of combining Pipelines, please refer to the advanced_swarmpy notebook