Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant Colony Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Application Artificial Bee Colony Algorithm (ABC) for Reconfiguring Distribution Network
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 01
Application of Artificial Bee Colony Algorithm to Software Testing
ASWEC '10 Proceedings of the 2010 21st Australian Software Engineering Conference
Image vector quantization algorithm via honey bee mating optimization
Expert Systems with Applications: An International Journal
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
A modified artificial bee colony algorithm
Computers and Operations Research
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
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Artificial bee colony ABC optimisation algorithm is relatively a recent and simple population-based probabilistic approach for global optimisation over continuous and discrete spaces. It has reportedly outperformed a few evolutionary algorithms EAs and other search heuristics when tested over both benchmark and real world problems. ABC, like other probabilistic optimisation algorithms, has inherent drawback of premature convergence or stagnation that leads to the loss of exploration and exploitation capability of ABC. Therefore, in order to find a trade-off between exploration and exploitation capability of ABC algorithm two modifications are proposed in this paper. First, a new control parameter namely, cognitive learning factor CLF is introduced in the employed bees phase and onlooker bees phase. Cognitive learning is a powerful mechanism that adjusts the current position of candidate solution by a means of some specified knowledge. Second, the range of ABC control parameter φ is modified. The proposed strategy named as balanced artificial bee colony BABC algorithm, balances the exploration and exploitation capability of the ABC. To prove efficiency of the algorithm, it is tested over 24 benchmark problems of different complexities and compared with the basic ABC.