Combinatorial Optimization Using Artificial Bee Colony Algorithm and Particle Swarm Optimization


ÖNDER E. , ÖZDEMİR M. , YILDIRIM B. F.

14th International Symposium on Econometrics Operations Research and Statistics, Bosnia And Herzegovina, 1 - 04 June 2013

  • Publication Type: Conference Paper / Full Text
  • Country: Bosnia And Herzegovina

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 (PSO) 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.