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"""! |
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@brief CCORE Wrapper for ant colony based algorithm for travelling salesman problem. |
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@authors Andrei Novikov, 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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from pyclustering.core.wrapper import *; |
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class c_antcolony_tsp_parameters(Structure): |
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""" |
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double q; |
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double ro; |
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double alpha; |
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double beta; |
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double gamma; |
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double initial_pheramone; |
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unsigned int iterations; |
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unsigned int ants_per_iteration; |
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""" |
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_fields_ = [("q" , c_double), |
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("ro" , c_double), |
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("alpha" , c_double), |
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("beta" , c_double), |
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("gamma" , c_double), |
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("qinit_pheramone" , c_double), |
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("iterations" , c_uint), |
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("ants_per_iteration" , c_uint) ]; |
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class c_antcolony_tsp_objects(Structure): |
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""" |
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unsigned int size; |
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unsigned int dimension; |
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double *data; |
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""" |
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_fields_ = [("size" , c_uint), |
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("dimension" , c_uint), |
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("data" , POINTER(c_double)) ]; |
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class c_antcolony_tsp_result(Structure): |
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""" |
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unsigned int size; |
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double path_length; |
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unsigned int *cities_num; |
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""" |
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_fields_ = [("size" , c_uint), |
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("path_length" , c_double), |
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("cities_num" , POINTER(c_uint)) ]; |
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def antcolony_tsp_process(cities, params): |
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dimension = len(cities[0]); |
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cities_coord = c_antcolony_tsp_objects(); |
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cities_coord.size = c_uint(len(cities) * dimension); |
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cities_coord.dimension = c_uint(dimension); |
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cities_coord.data = (c_double * cities_coord.size)(); |
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for i in range(0, cities_coord.size): |
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cities_coord.data[i] =cities[i // dimension][i % dimension]; |
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cities_coord = pointer(cities_coord); |
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algorithm_params = c_antcolony_tsp_parameters(); |
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algorithm_params.q = c_double(params.q); |
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algorithm_params.ro = c_double(params.ro); |
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algorithm_params.alpha = c_double(params.alpha); |
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algorithm_params.beta = c_double(params.beta); |
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algorithm_params.gamma = c_double(params.gamma); |
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algorithm_params.qinit_pheramone = c_double(params.qinit_pheramone); |
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algorithm_params.iterations = c_uint(params.iterations); |
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algorithm_params.ants_per_iteration = c_uint(params.ants_per_iteration); |
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algorithm_params = pointer(algorithm_params); |
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64); |
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result = ccore.ant_colony_TSP(cities_coord, algorithm_params); |
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result = cast(result, POINTER(c_antcolony_tsp_result))[0]; |
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return result; |
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# cities = [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0]]; |
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# |
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# params = c_antcolony_tsp_parameters(); |
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# params.q = 1.5; |
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# params.ro = 0.7; |
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# params.alpha = 1.0; |
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# params.beta = 1.0; |
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# params.gamma = 2.0; |
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# params.qinitial_pheramone = 0.1; |
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# params.iterations = 50; |
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# params.ants_per_iteration = 10; |
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# |
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# res = antcolony_tsp_process(cities, params) |
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# |
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# print ("Result :") |
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# print (res.size) |
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# print (res.path_length) |
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# for i in range(res.size): |
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# print (res.cities_num[i]) |
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annoviko /
pyclustering
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