Journal of Siberian Federal University. Mathematics & Physics / Self-configuring Nature Inspired Algorithms for Combinatorial Optimization Problems

Full text (.pdf)
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