An Improved Artificial Bee Colony Algorithm Based on Gaussian Mutation and Chaos Disturbance

摘要

Artificial Bee Colony (ABC) algorithm is a novel bio-inspired swarm intelligence approach which is competitive with other population-based algorithms and has the advantage of using fewer control parameters. However, basic ABC is easy to be prematurely convergent and be trapped into local optimum. In the later iteration, algorithm has low convergent speed and population diversity seriously decreases. In this paper, Gaussian mutation and chaos disturbance are introduced into ABC to overcome the shortcomings above. Applications of improved ABC algorithm on four benchmark optimization functions show marked improvement in performance over the basic ABC.