Geodesic
Self-configuring nature inspired algorithms for combinatorial optimization problems
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 10 (2017) no. 4, pp. 463-473

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In this work authors introduce and study the self-configuring Genetic Algorithm (GA) and the self-configuring Ant Colony Optimization (ACO) algorithm and apply them to one of the most known combinatorial optimization task — Travelling Salesman Problem (TSP). The estimation of suggested algorithms performance is fulfilled on well-known benchmark TSP and then compared with other heuristics such as Lin–Kernigan (3-opt local search) and Intelligent Water Drops algorithm (IWDs). Numerical experiments show that suggested approach demonstrates the competitive performance. Both adaptive algorithms show good results on these problems as they outperform other algorithms with their settings with average performance.
Keywords: travelling Salesman problem, genetic algorithm, ant colony optimization, intelligent water drops algorithm, self-configuration. 
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     author = {Olga Ev. Semenkina and Eugene A. Popov and Olga Er. Semenkina},
     title = {Self-configuring nature inspired algorithms for combinatorial optimization problems},
     journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
     pages = {463--473},
     publisher = {mathdoc},
     volume = {10},
     number = {4},
     year = {2017},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a7/}
}
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Olga Ev. Semenkina; Eugene A. Popov; Olga Er. Semenkina. Self-configuring nature inspired algorithms for combinatorial optimization problems. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 10 (2017) no. 4, pp. 463-473. http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a7/