Self-configuring nature inspired algorithms for combinatorial optimization problems

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.