A Hybrid Artificial Bee Colony algorithm for numerical function optimization

Abstract

Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence algorithms which used for problem optimization. This paper presents a Hybrid Artificial Bee Colony algorithm (HABC), in which the crossover operator of Genetic Algorithm is introduced, to improve the canonical ABC in solving complex optimization problems. The variation of the algorithm is presented and seven benchmark functions are used to check its validity. The simulation results showed that the proposed HABC outperforms the canonical ABC and Particle Swarm Optimization algorithms on most functions, especially on the multimodal functions.