Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Ant Colony Optimization
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Particle swarm approach for structural design optimization
Computers and Structures
Journal of Global Optimization
Ant colony optimization for multi-objective flow shop scheduling problem
Computers and Industrial Engineering
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
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
2D image registration using focused mutual information for application in dentistry
Computers in Biology and Medicine
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
The performance and sensitivity of the parameters setting on the best-so-far ABC
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
Adaptive artificial bee colony optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Balanced artificial bee colony algorithm
International Journal of Artificial Intelligence and Soft Computing
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
A swarm optimization algorithm inspired in the behavior of the social-spider
Expert Systems with Applications: An International Journal
A novel artificial bee colony algorithm with Powell's method
Applied Soft Computing
A new algorithm inspired in the behavior of the social-spider for constrained optimization
Expert Systems with Applications: An International Journal
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The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm.