Geodesic
PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 13 (2020) no. 4, pp. 107-118 Cet article a éte moissonné depuis la source Math-Net.Ru

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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.
Keywords: ant colony optimization, neighbour joining method, traveling salesman problem, parallel algorithm, multi-core system. 
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     title = {PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a, Matemati\v{c}eskoe modelirovanie i programmirovanie},
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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 Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 13 (2020) no. 4, pp. 107-118. http://geodesic.mathdoc.fr/item/VYURU_2020_13_4_a8/

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