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"""!
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@brief TSP algorithm: ant colony based
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@details Based on article description:
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- M.Dorigo, L.M.Gambardella. Ant colonies for the traveling salesman problem. 1996.
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- J.Yang, X.Shi, M.Marchese, Y.Liang. An ant colony optimization method for generalized TSP problem. 2008.
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@authors Alexey Kukushkin ([email protected])
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@date 2014-2016
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@copyright GNU Public License
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@cond GNU_PUBLIC_LICENSE
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PyClustering is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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PyClustering is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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@endcond
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"""
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import pyclustering.core.antcolony_tsp_wrapper as wrapper;
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class antcolony_parameters:
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"""!
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@brief Describes parameters of ant colony based algorithm for TSP problem.
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@see antcolony
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"""
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q = 1.5;
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ro = 0.7;
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alpha = 1.0;
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beta = 1.0;
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gamma = 2.0;
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qinit_pheramone = 0.1;
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ants_per_iteration = 10;
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iterations = 50;
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class antcolony:
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"""!
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@brief Simulates ant colony to solve travelling salesman problem (TSP).
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@details Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated
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in the form of a pheromone trail deposited on the edges of the TSP graph.
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@warning Solution is performed only via CCORE library (C/C++ implementation of the library).
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Example:
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@code
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@endcode
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"""
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__parameters = None;
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__result_tsp = None;
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def __init__(self, parameters):
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"""!
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@brief Constructor of ant colony based algorithm for travelling salesman problem.
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@param[in] parameters (antcolony_parameters): Parameters of the ant colony algorithm.
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"""
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self.__parameters = parameters;
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def process(self, object_locations):
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"""!
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@brief Perform simulation of ant colony to solve travelling salesman problem.
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@param[in] object_locations (list): Coordinates of objects that should be visited.
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"""
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self.__result_tsp = wrapper.antcolony_tsp_process(object_locations);
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annoviko /
pyclustering
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