Journal of Global Optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
An Improved Harmony Search Algorithm with Differential Mutation Operator
Fundamenta Informaticae - Swarm Intelligence
Parameter Tuning for the Artificial Bee Colony Algorithm
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Inter-particle communication and search-dynamics of lbest particle swarm optimizers: An analysis
Information Sciences: an International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
An artificial bee colony algorithm for the maximally diverse grouping problem
Information Sciences: an International Journal
A harmony search algorithm for nurse rostering problems
Information Sciences: an International Journal
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
Computational Optimization and Applications
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The class of foraging algorithms is a relatively new field based on mimicking the foraging behavior of animals, insects, birds or fish in order to develop efficient optimization algorithms. The artificial bee colony (ABC), the bees algorithm (BA), ant colony optimization (ACO), and bacterial foraging optimization algorithms (BFOA) are examples of this class to name a few. This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. To the best of our knowledge, most of the work conducted so far using foraging algorithms has been tested on classical functions. This work provides the comparison using the well-known CEC05 benchmark functions based on the solution reached, the success rate, and the performance rate.