A novel artificial bee colony based on Gaussian sampling

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.

Keywords:

• Artificial bee colony
• Swarm intelligence
• Gaussian sampling
• Search strategy
• Global optimization