ABC

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.


References in zbMATH (referenced in 289 articles )

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  1. Stolpe, Mathias: To bee or not to bee -- comments on “Discrete optimum design of truss structures using artificial bee colony algorithm” (2011) ioport
  2. Szeto, W. Y.; Wu, Yongzhong; Ho, Sin C.: An artificial bee colony algorithm for the capacitated vehicle routing problem (2011) ioport
  3. Tasgetiren, M. Fatih; Pan, Quan-Ke; Suganthan, P. N.; Chen, Angela H-L: A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops (2011) ioport
  4. Yeh, Wei-Chang; Hsieh, Tsung-Jung: Solving reliability redundancy allocation problems using an artificial bee colony algorithm (2011) ioport
  5. Zou, Wenping; Zhu, Yunlong; Chen, Hanning; Zhang, Beiwei: Solving multiobjective optimization problems using artificial bee colony algorithm (2011)
  6. Aderhold, Andrej; Diwold, Konrad; Scheidler, Alexander; Middendorf, Martin: Artificial bee colony optimization: a new selection scheme and its performance (2010)
  7. Ahrari, Ali; Ahrari, Reza: On the utility of randomly generated functions for performance evaluation of evolutionary algorithms (2010)
  8. Ahrari, Ali; Saadatmand, Mohammad R.; Shariat-Panahi, Masoud; Atai, Ali A.: On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms (2010)
  9. Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush: A novel bee swarm optimization algorithm for numerical function optimization (2010)
  10. Chen, Hanning; Zhu, Yunlong; Hu, Kunyuan: Discrete and continuous optimization based on multi-swarm coevolution (2010)
  11. Chen, Hanning; Zhu, Yunlong; Hu, Kunyuan; He, Xiaoxian: Hierarchical swarm model: a new approach to optimization (2010)
  12. Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel: Protein structure prediction using bee colony optimization metaheuristic (2010) ioport
  13. Horng, Ming-Huwi: A multilevel image thresholding using the honey bee mating optimization (2010)
  14. Li, Huanzhe; Liu, Kunqi; Li, Xia: A comparative study of artificial bee colony, bees algorithms and differential evolution on numerical benchmark problems (2010)
  15. Marinakis, Yannis; Marinaki, Magdalene; Dounias, Georgios: Honey bees mating optimization algorithm for large scale vehicle routing problems (2010)
  16. Moradi, Shapour; Fatahi, Laleh; Razi, Pejman: Finite element model updating using bees algorithm (2010) ioport
  17. Özbakir, Lale; Baykasoğlu, Adil; Tapkan, Pınar: Bees algorithm for generalized assignment problem (2010)
  18. Sundar, Shyam; Singh, Alok: A swarm intelligence approach to the quadratic minimum spanning tree problem (2010) ioport
  19. Vakil-Baghmisheh, Mohammad-Taghi; Salim, Mina: The design of PID controllers for a Gryphon robot using four evolutionary algorithms: a comparative study (2010) ioport
  20. Zhu, Guopu; Kwong, Sam: Gbest-guided artificial bee colony algorithm for numerical function optimization (2010)

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Further publications can be found at: http://mf.erciyes.edu.tr/abc/publ.htm