An Enhanced Hybridized Artificial Bee Colony Algorithm for Optimization Problems

Abstract

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm and there still exist some problems it cannot solve very well.This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems.The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm (GA) to ABC in the mutation bee phase.In addition, to enhance the accuracy performance of ABC, when producing the initial population, the opposition-based learning method is employed.Experiments are conducted on a set of 6 benchmark functions.The results demonstrate good performance of the proposed approach in solving complex numerical optimization problems over other four ABC variants.