Genetic Colony Algorithm And Its Application

Artificial bee colony (ABC) algorithm introduced by D.Karaboga was inspired by the behaviors of real honey bee colonies. The solutions of the whole swarm are exploited based on the neighbor information by employed bees and onlookers in the ABC algorithm. The classical artificial bee colony algorithm as a relatively new swarm optimization method is often trapped in local optima in global optimization. In this paper we propose a hybrid algorithm based on genetic algorithm (GA) and artificial bee colony (ABC) algorithm. In this hybrid procedure, crossover operator and mutation operator of GA are introduced to improve the ABC in solving complex optimization problems. In the paper, experiments for traveling salesman problem and function optimization problems show that the proposed algorithm is efficient compared with other techniques in recent literature.