Abstract

Combinatorial  optimization  problems  are  usually  NP‐hard and the solution space of them is very large. Therefore the set of feasible solutions cannot be evaluated one by one. Artificial Bee Colony (ABC) and Particle Swarm Optimization algorithms, meta‐heuristics  for  combinatorial  optimization  problems,  are  swarm intelligence based approaches and they are nature‐inspired optimization algorithms. In this study ABC and PSO techniques were used for finding the shortest route in condition of  to  visit  every  city  one  time  but  the  starting  city  twice.  The  problem is a well known Symmetric Travelling Salesman Problem.  The  TSP  of  visiting  81  cities  in  Turkey  was  solved.  ABC‐based and PSO‐based algorithms are applied to solve the travelling salesman problem and results are compared with ant colony  optimization  (ACO)  solution.  Our  research  mainly  focused on the application of ABC and PSO algorithms in combinatorial  optimization  problem.  Numerical  experiments  show that ABC and PSO are very competitive and have good results  compared  with  the  ACO,  when  it  is  applied  to  the  test  problem.

Keywords: Artificial Bee Colony Algorithm, Combinatorial Problems, Meta-Heuristics, Particle Swarm Optimization, Shortest Path, Traveling Salesman Problem