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. Mezura-Montes, Efrén; Cetina-Domínguez, Omar: Empirical analysis of a modified Artificial Bee Colony for constrained numerical optimization (2012)
  2. Pavone, Mario; Narzisi, Giuseppe; Nicosia, Giuseppe: Clonal selection: an immunological algorithm for global optimization over continuous spaces (2012)
  3. Shao, Yichuan; Yao, Xingjia; Tian, Liwei; Chen, Hanning: A multiswarm optimizer for distributed decision making in virtual enterprise risk management (2012)
  4. Sundar, Shyam; Singh, Alok: A hybrid heuristic for the set covering problem (2012)
  5. Tapkan, Pınar; Lale, Özbakır; Baykasoğlu, Adil: Bees algorithm for constrained fuzzy multi-objective two-sided assembly line balancing problem (2012)
  6. Tien, Jia-Ping; Li, Tzuu-Hseng S.: Hybrid Taguchi-chaos of multilevel immune and the artificial bee colony algorithm for parameter identification of chaotic systems (2012)
  7. Wu, Bin; Qian, Cunhua; Ni, Weihong; Fan, Shuhai: Hybrid harmony search and artificial bee colony algorithm for global optimization problems (2012)
  8. Yin, Yunqiang; Wu, Wen-Hsiang; Cheng, Shuenn-Ren; Wu, Chin-Chia: An investigation on a two-agent single-machine scheduling problem with unequal release dates (2012)
  9. Zhang, Hao; Zhu, Yunlong; Zou, Wenping; Yan, Xiaohui: A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production (2012)
  10. Zheng, Yu-Jun; Ling, Hai-Feng; Guan, Qiu: Adaptive parameters for a modified comprehensive learning particle swarm optimizer (2012) ioport
  11. Alzaqebah, Malek; Abdullah, Salwani: Hybrid artificial bee colony search algorithm based on disruptive selection for examination timetabling problems (2011)
  12. Davidović, Tatjana; Ramljak, Dušan; Šelmić, Milica; Teodorović, Dušan: Bee colony optimization for the (p)-center problem (2011)
  13. Gao, Weifeng; Liu, Sanyang: Improved artificial bee colony algorithm for global optimization (2011)
  14. Gao, Wei-Feng; Liu, San-Yang; Jiang, Fei: An improved artificial bee colony algorithm for directing orbits of chaotic systems (2011)
  15. Gozde, Haluk; Taplamacioglu, M. Cengiz: Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system (2011)
  16. Kang, Fei; Li, Junjie; Ma, Zhenyue: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011)
  17. Marinakis, Yannis; Marinaki, Magdalene; Dounias, Georgios: Honey bees mating optimization algorithm for the Euclidean traveling salesman problem (2011) ioport
  18. Pan, Quan-Ke; Tasgetiren, M. Fatih; Suganthan, P. N.; Chua, T. J.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem (2011) ioport
  19. Singh, Alok; Sundar, Shyam: An artificial bee colony algorithm for the minimum routing cost spanning tree problem (2011) ioport
  20. Sonmez, Mustafa: Discrete optimum design of truss structures using artificial bee colony algorithm (2011) ioport

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