On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
An artificial bee colony approach for clustering
Expert Systems with Applications: An International Journal
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
Artificial bee colony algorithm for small signal model parameter extraction of MESFET
Engineering Applications of Artificial Intelligence
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
A modified artificial bee colony algorithm
Computers and Operations Research
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
A swarm optimization algorithm inspired in the behavior of the social-spider
Expert Systems with Applications: An International Journal
A new algorithm inspired in the behavior of the social-spider for constrained optimization
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 0.01 |
Artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms.