An introduction to genetic algorithms
An introduction to genetic algorithms
The computational beauty of nature
The computational beauty of nature
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Journal of Global Optimization
Nature Inspired Intelligence for the Constrained Portfolio Optimization Problem
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Generation of pairwise test sets using a simulated bee colony algorithm
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
Honey Bees Mating Optimization algorithm for large scale vehicle routing problems
Natural Computing: an international journal
Honey Bees Mating Optimization algorithm for financial classification problems
Applied Soft Computing
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
Applied Soft Computing
Discrete optimum design of truss structures using artificial bee colony algorithm
Structural and Multidisciplinary Optimization
A hybrid 'bee(s) algorithm' for solving container loading problems
Applied Soft Computing
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Bee colony intelligence in zone constrained two-sided assembly line balancing problem
Expert Systems with Applications: An International Journal
Enhanced artificial bee colony algorithm performance
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Information Sciences: an International Journal
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
On the application of bees algorithm to the problem of crack detection of beam-type structures
Computers and Structures
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization
Expert Systems with Applications: An International Journal
International Journal of Computer Applications in Technology
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic 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
A survey on optimization metaheuristics
Information Sciences: an International Journal
An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Journal of Intelligent Manufacturing
Integrating the artificial bee colony and bees algorithm to face constrained optimization problems
Information Sciences: an International Journal
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Many engineering applications often involve the minimization of some objective functions. In the case of multilevel optimizations or functions with many local minimums, the optimization becomes very difficult. Biology-inspired algorithms such as genetic algorithms are more effective than conventional algorithms under appropriate conditions. In this paper, we intend to develop a new virtual bee algorithm (VBA) to solve the function optimizations with the application in engineering problems. For the functions with two-parameters, a swarm of virtual bees are generated and start to move randomly in the phase space. These bees interact when they find some target nectar corresponding to the encoded values of the function. The solution for the optimization problem can be obtained from the intensity of bee interactions. The simulations of the optimization of De Jong's test function and Keane's multi-peaked bumpy function show that the one agent VBA is usually as effective as genetic algorithms and multiagent implementation optimizes more efficiently than conventional algorithms due to the parallelism of the multiple agents. Comparison with the other algorithms such as genetic algorithms will also be discussed in detail.