A hybrid artificial bee colony optimization algorithm

Abstract

Artificial bee colony (ABC) algorithm introduced by D. Karaboga was inspired by the behaviors of real honey bee colonies. The routes of the swarm are exploited according to the neighbor information by employed bees and onlookers in the ABC algorithm. The classic artificial bee colony algorithm as a swarm optimization method is sometimes trapped in local optima. In this paper we propose a hybrid algorithm based on ABC algorithm and genetic algorithm. In the hybrid procedure, the crossover operator and mutation operator of genetic algorithm are introduced to improve the ABC algorithm in solving complex optimization problems. In the paper, the experiments for Traveling Salesman Problem and function optimization problems show that the proposed algorithm is more efficient compared with other techniques in recent literature.