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 300 articles )

Showing results 61 to 80 of 300.
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  1. Dolatabadi, Soheil: Weighted vertices optimizer (WVO): a novel metaheuristic optimization algorithm (2018)
  2. Dunder, Emre; Gumustekin, Serpil; Cengiz, Mehmet Ali: Variable selection in gamma regression models via artificial bee colony algorithm (2018)
  3. Du, Wenbo; Zhang, Mingyuan; Ying, Wen; Perc, Matjaž; Tang, Ke; Cao, Xianbin; Wu, Dapeng: The networked evolutionary algorithm: a network science perspective (2018)
  4. Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru: A new hybrid algorithm for continuous optimization problem (2018)
  5. Gao, Yangjun; Zhang, Fengming; Zhao, Yu; Li, Chao: Quantum-inspired wolf pack algorithm to solve the 0-1 knapsack problem (2018)
  6. García-Nieto, P. J.; García-Gonzalo, E.; Alonso Fernández, J. R.; Díaz Muñiz, C.: Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach (2018)
  7. García Nieto, P. J.; García-Gonzalo, E.; Álvarez Antón, J. C.; González Suárez, V. M.; Mayo Bayón, R.; Mateos Martín, F.: A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance (2018)
  8. Hu, Hui; Cai, Zhaoquan; Hu, Song; Cai, Yingxue; Chen, Jia; Huang, Sibo: Improving monarch butterfly optimization algorithm with self-adaptive population (2018)
  9. Jin, Ye; Sun, Yuehong; Ma, Hongjiao: A developed artificial bee colony algorithm based on cloud model (2018)
  10. Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar: An approach for reduction of false predictions in reverse engineering of gene regulatory networks (2018)
  11. Kuldeep, B.; Kumar, A.; Singh, G. K.; Lee, Heung-No: Design of multichannel filter bank using minor component analysis and fractional derivative constraints (2018)
  12. Kvasov, Dmitri E.; Mukhametzhanov, Marat S.: Metaheuristic vs. deterministic global optimization algorithms: the univariate case (2018)
  13. Lalou, Mohammed; Tahraoui, Mohammed Amin; Kheddouci, Hamamache: The critical node detection problem in networks: a survey (2018)
  14. Liu, Hanbing; He, Xin; Jiao, Yubo: Damage identification algorithm of hinged joints for simply supported slab bridges based on modified hinge plate method and artificial bee colony algorithms (2018)
  15. Mandal, S.: Elephant swarm water search algorithm for global optimization (2018)
  16. Nayak, Janmenjoy; Naik, Bighnaraj: A novel honey-bees mating optimization approach with higher order neural network for classification (2018)
  17. Pandiri, Venkatesh; Singh, Alok: A hyper-heuristic based artificial bee colony algorithm for (k)-interconnected multi-depot multi-traveling salesman problem (2018)
  18. Pang, Bao; Song, Yong; Zhang, Chengjin; Wang, Hongling; Yang, Runtao: A modified artificial bee colony algorithm based on the self-learning mechanism (2018)
  19. Panniem, Amnat; Puphasuk, Pikul: A modified artificial bee colony algorithm with firefly algorithm strategy for continuous optimization problems (2018)
  20. Ramadas, Gisela C. V.; Fernandes, Edite M. G. P.; Ramadas, António M. V.; Rocha, Ana Maria A. C.; Costa, M. Fernanda P.: On metaheuristics for solving the parameter estimation problem in dynamic systems: a comparative study (2018)

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