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.

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  1. García Nieto, P.J.; García-Gonzalo, E.; Alonso Fernández, J.R.; Díaz Muñiz, C.: A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (northern Spain) (2017)
  2. Duan, Qibin; Kroese, Dirk P.: Splitting for optimization (2016)
  3. Ferjani, Ayet Allah; Liouane, Noureddine; Borne, Pierre: Logic gate-based evolutionary algorithm for the multidimensional knapsack problem-wireless sensor network application (2016)
  4. Goudos, Sotirios K.: A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems (2016)
  5. Wang, Rui; Zhou, Yongquan; Qiao, Shilei; Huang, Kang: Flower pollination algorithm with bee pollinator for cluster analysis (2016) ioport
  6. Alkaya, Ali Fuat; Duman, Ekrem: Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly (2015)
  7. Ergezer, M.; Simon, D.: Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms (2015)
  8. Fortier, Nathan; Sheppard, John; Strasser, Shane: Abductive inference in Bayesian networks using distributed overlapping swarm intelligence (2015) ioport
  9. Hei, Yongqiang; Li, Wentao; Fu, Weihong; Li, Xiaohui: Efficient parallel artificial bee colony algorithm for cooperative spectrum sensing optimization (2015) ioport
  10. Hu, Wei; Yu, Yongguang; Zhang, Shuo: A hybrid artificial bee colony algorithm for parameter identification of uncertain fractional-order chaotic systems (2015)
  11. Liang, Xiaolei; Li, Wenfeng; Zhang, Yu; Zhou, MengChu: An adaptive particle swarm optimization method based on clustering (2015) ioport
  12. Liao, Chung-Shou; Hsieh, Tsung-Jung; Guo, Xian-Chang; Liu, Jian-Hong; Chu, Chia-Chi: Hybrid search for the optimal PMU placement problem on a power grid (2015)
  13. Li, Xinbin; Lu, Lu; Liu, Lei; Li, Guoqiang; Guan, Xinping: Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm (2015) ioport
  14. Ma, Lianbo; Hu, Kunyuan; Zhu, Yunlong; Chen, Hanning: A hybrid artificial bee colony optimizer by combining with life-cycle, Powell’s search and crossover (2015)
  15. Marinakis, Yannis; Marinaki, Magdalene: Combinatorial neighborhood topology bumble bees mating optimization for the vehicle routing problem with stochastic demands (2015) ioport
  16. Mernik, Marjan; Liu, Shih-Hsi; Karaboga, Dervis; Črepinšek, Matej: On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation (2015)
  17. Monfort, Alain; Renne, Jean-Paul; Roussellet, Guillaume: A quadratic Kalman filter (2015)
  18. Szeto, W.Y.; Jiang, Y.; Wang, D.Z.W.; Sumalee, A.: A sustainable road network design problem with land use transportation interaction over time (2015)
  19. Tonyali, Samet; Alkaya, Ali Fuat: Application of recently proposed metaheuristics to the sequence dependent TSP (2015) ioport
  20. Wang, Zutong; Guo, Jiansheng; Zheng, Mingfa; Wang, Ying: Uncertain multiobjective traveling salesman problem (2015)

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