Combinatorial Optimization Using Particle Swarm Optimization based Genetic Algorithm and Artificial Bee Colony Algorithm

Önder E., ÖZDEMİR M. , Yıldırım B. F.

14th International Symposium on Econometrics Operations Research and Statistics, Bosnia And Herzegovina, 1 - 04 May 2013, pp.1-12

  • Publication Type: Conference Paper / Full Text
  • Country: Bosnia And Herzegovina
  • Page Numbers: pp.1-12


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), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are meta-heuristics techniques for combinatorial optimization problems. ABC and PSO are swarm intelligence based approaches and they are nature-inspired optimization algorithms. In this study ABC and PSO based GA 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 and PSO-based GA 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 based GA algorithms in combinatorial optimization problem. Numerical experiments show that ABC and PSO based GA are very competitive and have good results compared with the ACO, when it is applied to the regarding problem.