The particle swarm: social adaptation in information-processing systems
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Transportation Modeling: An Artificial Life Approach
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Routing and wavelength assignment in all-optical networks based on the bee colony optimization
AI Communications - Network Analysis in Natural Sciences and Engineering
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
Scheduling independent tasks: Bee Colony Optimization approach
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Bee colony optimization for the p-center problem
Computers and Operations Research
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bee colony optimization for scheduling independent tasks to identical processors
Journal of Heuristics
Transit network design by Bee Colony Optimization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The Bee Colony Optimization (BCO) meta-heuristic deals with combinatorial optimization problems. It is biologically inspired method that explores collective intelligence applied by the honey bees during nectar collecting process. In this paper we perform empirical study of the BCO algorithm. We apply BCO to optimize numerous numerical test functions. The obtained results are compared with the results in the literature. The numerical experiments performed on well-known benchmark functions show that the BCO is competitive with other methods and it can generate high-quality solutions within negligible CPU times.