Vestnik YuUrGU. Ser. Mat. Model. Progr., 2020, Volume 13, Issue 4, Pages 107–118
Programming & Computer Software
PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system
W. B. Yahiaa, M. W. Al-Neamab, G. E. Arifa
a Tikrit University, Tikrit, Iraq
b Mosul University, Mosul, Iraq
One of the most interesting and relevant approaches for solving optimization problems are parallel algorithms that work simultaneously with a large number of tasks. The paper presents a new parallel algorithm for NACO that is a hybrid algorithm that consists of the Ant Colony Optimization method combined with the Neighbour Joining method to get accurate and efficient results when solving the Traveling Salesman Problem. Through carrying out comprehensive experiments using a wide variety of real dataset sizes and the multi-core system, the practical results show that the developed program outperforms NACO in terms of execution time and consumed storage space. Availability and implementation: source codes in MATLAB 2017 are publicly available at Internet.
ant colony optimization, neighbour joining method, traveling salesman problem, parallel algorithm, multi-core system.
PDF file (741 kB)
MSC: 05C10, 03D15, 68W10
W. B. Yahia, M. W. Al-Neama, G. E. Arif, “PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 13:4 (2020), 107–118
Citation in format AMSBIB
\by W.~B.~Yahia, M.~W.~Al-Neama, G.~E.~Arif
\paper PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system
\jour Vestnik YuUrGU. Ser. Mat. Model. Progr.
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