Combinatorial Optimization Using Artificial Bee Colony Algorithm and Particle Swarm Optimization Supported Genetic Algorithm
Kafkas University Journal of Economics and Administrative Sciences Faculty, Vol. 4, Issue 6, 2013
12 Pages Posted: 23 Jan 2014
Date Written: January 21, 2014
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 metaheuristic 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 supported 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 Traveling Salesman Problem. Our traveling salesman problem (TSP) consists of 81 cities of Turkey. 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 supported GA are very competitive and have good results compared with the ACO, when it is applied to the regarding problem.
Keywords: Artificial Bee Colony Algorithm, Particle Swarm Optimization, Clustering, Genetic Algorithm, Traveling Salesman Problem, Shortest Path, Meta-Heuristics, Combinatorial Problems
JEL Classification: C61
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