Journal of Global Optimization
Ant Colony Optimization
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
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
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
An artificial bee colony algorithm for the maximally diverse grouping problem
Information Sciences: an International Journal
An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce
Journal of Electronic Commerce in Organizations
International Journal of Wireless and Mobile Computing
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
Computational Optimization and Applications
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
| Hi-index | 7.29 |
The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in the ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose a modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation. In addition, to enhance the global convergence, when producing the initial population and scout bees, both chaotic systems and opposition-based learning method are employed. Experiments are conducted on a set of 26 benchmark functions. The results demonstrate good performance of ABC/best in solving complex numerical optimization problems when compared with two ABC based algorithms.