Abstract
Artificial bee colony (ABC) is an efficient optimization tool, which is inspired by swarm intelligence. Recent studies show that ABC is competitive to other famous bio-inspired optimization algorithms. However, ABC is good at global search, but poor at local search. To overcome this problem, a new ABC variant based on Gaussian sampling is proposed. Experiments are carried out on ten well-known benchmark functions. Computational results show that our approach outperforms the standard ABC and two other improved ABC algorithms.