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. Yang, Li; Ye, Zhi-sheng; Lee, Chi-Guhn; Yang, Su-fen; Peng, Rui: A two-phase preventive maintenance policy considering imperfect repair and postponed replacement (2019)
  2. Yang, Li; Zhao, Yu; Ma, Xiaobing: Group maintenance scheduling for two-component systems with failure interaction (2019)
  3. Abazari, Ahmadreza; Dozein, Mehdi Ghazavi; Monsef, Hassan: An optimal fuzzy-logic based frequency control strategy in a high wind penetrated power system (2018)
  4. Agarwalla, P.; Mukhopadhyay, S.: Feature selection using multi-objective optimization technique for supervised cancer classification (2018)
  5. Ali, Javaid; Saeed, Muhammad; Rafiq, Muhammad; Iqbal, Shaukat: Numerical treatment of nonlinear model of virus propagation in computer networks: an innovative evolutionary Padé approximation scheme (2018)
  6. Al-Salamah, Muhammad: Economic production quantity with the presence of imperfect quality and random machine breakdown and repair based on the artificial bee colony heuristic (2018)
  7. Brahimi, Nassim; Salhi, Abdellah; Ourbih-Tari, Megdouda: Convergence analysis of the plant propagation algorithm for continuous global optimization (2018)
  8. Camarena, Octavio; Cuevas, Erik; Pérez-Cisneros, Marco; Fausto, Fernando; González, Adrián; Valdivia, Arturo; Rodriguez-Tello, Eduardo: LS-II: an improved locust search algorithm for solving optimization problems (2018)
  9. De Vincenzo, Ilario; Massari, Giovanni F.; Giannoccaro, Ilaria; Carbone, Giuseppe; Grigolini, Paolo: Mimicking the collective intelligence of human groups as an optimization tool for complex problems (2018)
  10. Dolatabadi, Soheil: Weighted vertices optimizer (WVO): a novel metaheuristic optimization algorithm (2018)
  11. Dunder, Emre; Gumustekin, Serpil; Cengiz, Mehmet Ali: Variable selection in gamma regression models via artificial bee colony algorithm (2018)
  12. Du, Wenbo; Zhang, Mingyuan; Ying, Wen; Perc, Matjaž; Tang, Ke; Cao, Xianbin; Wu, Dapeng: The networked evolutionary algorithm: a network science perspective (2018)
  13. Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru: A new hybrid algorithm for continuous optimization problem (2018)
  14. Gao, Yangjun; Zhang, Fengming; Zhao, Yu; Li, Chao: Quantum-inspired wolf pack algorithm to solve the 0-1 knapsack problem (2018)
  15. 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)
  16. 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)
  17. Hu, Hui; Cai, Zhaoquan; Hu, Song; Cai, Yingxue; Chen, Jia; Huang, Sibo: Improving monarch butterfly optimization algorithm with self-adaptive population (2018)
  18. Jin, Ye; Sun, Yuehong; Ma, Hongjiao: A developed artificial bee colony algorithm based on cloud model (2018)
  19. Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar: An approach for reduction of false predictions in reverse engineering of gene regulatory networks (2018)
  20. Kuldeep, B.; Kumar, A.; Singh, G. K.; Lee, Heung-No: Design of multichannel filter bank using minor component analysis and fractional derivative constraints (2018)

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