%P 367-380
%T Combinatorial Optimization: Comparison of Heuristic Algorithms in Travelling Salesman Problem
%I Springer Netherlands
%V 26
%A A.H. Halim
%A I. Ismail
%O cited By 76
%J Archives of Computational Methods in Engineering
%L scholars11637
%D 2019
%N 2
%R 10.1007/s11831-017-9247-y
%K Ant colony optimization; Combinatorial optimization; Computational complexity; Genetic algorithms; Nearest neighbor search; Philosophical aspects; Problem solving; Simulated annealing; Tabu search; Traveling salesman problem; Trees (mathematics), Intensification and diversifications; Meta heuristic algorithm; Nearest neighbors; Optimization algorithms; Satisfactory solutions; Specific problems; Travelling salesman problem; Travelling salesman problem (TSP), Heuristic algorithms
%X The Travelling Salesman Problem (TSP) is an NP-hard problem with high number of possible solutions. The complexity increases with the factorial of n nodes in each specific problem. Meta-heuristic algorithms are an optimization algorithm that able to solve TSP problem towards a satisfactory solution. To date, there are many meta-heuristic algorithms introduced in literatures which consist of different philosophies of intensification and diversification. This paper focuses on 6 heuristic algorithms: Nearest Neighbor, Genetic Algorithm, Simulated Annealing, Tabu Search, Ant Colony Optimization and Tree Physiology Optimization. The study in this paper includes comparison of computation, accuracy and convergence. © 2017, CIMNE, Barcelona, Spain.