- Issue
- Journal of Siberian Federal University. Mathematics & Physics. 2017 10 (4)
- Authors
- Semenkina, Olga Ev.; Popov, Eugene A.; Semenkina, Olga Er.
- Contact information
- Semenkina, Olga Ev.: Siberian State Aerospace University Krasnoyarsky rabochy, 31, Krasnoyarsk, 660037 Russia; ; Popov, Eugene A.: Siberian State Aerospace University Krasnoyarsky rabochy, 31, Krasnoyarsk, 660037 Russia; ; Semenkina, Olga Er.: Siberian State Aerospace University Krasnoyarsky rabochy, 31, Krasnoyarsk, 660037 Russia;
- Keywords
- travelling Salesman problem; genetic algorithm; ant colony optimization; intelligent water drops algorithm; self-configuration
- Abstract
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 combi- natorial 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 per- formance
- Pages
- 463-473
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/34758
Journal of Siberian Federal University. Mathematics & Physics / Self-configuring Nature Inspired Algorithms for Combinatorial Optimization Problems
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