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 124 articles )

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  1. Ferjani, Ayet Allah; Liouane, Noureddine; Borne, Pierre: Logic gate-based evolutionary algorithm for the multidimensional knapsack problem-wireless sensor network application (2016)
  2. Goudos, Sotirios K.: A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems (2016)
  3. Wang, Rui; Zhou, Yongquan; Qiao, Shilei; Huang, Kang: Flower pollination algorithm with bee pollinator for cluster analysis (2016)
  4. Alkaya, Ali Fuat; Duman, Ekrem: Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly (2015)
  5. Ergezer, M.; Simon, D.: Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms (2015)
  6. Hei, Yongqiang; Li, Wentao; Fu, Weihong; Li, Xiaohui: Efficient parallel artificial bee colony algorithm for cooperative spectrum sensing optimization (2015)
  7. Hu, Wei; Yu, Yongguang; Zhang, Shuo: A hybrid artificial bee colony algorithm for parameter identification of uncertain fractional-order chaotic systems (2015)
  8. 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)
  9. Monfort, Alain; Renne, Jean-Paul; Roussellet, Guillaume: A quadratic Kalman filter (2015)
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  11. Tonyali, Samet; Alkaya, Ali Fuat: Application of recently proposed metaheuristics to the sequence dependent TSP (2015)
  12. Wang, Zutong; Guo, Jiansheng; Zheng, Mingfa; Wang, Ying: Uncertain multiobjective traveling salesman problem (2015)
  13. Alzaqebah, M.; Abdullah, S.: An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling (2014)
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  15. Biswas, Subhodip; Das, Swagatam; Kundu, Souvik; Patra, Gyana Ranjan: Utilizing time-linkage property in DOPs: an information sharing based artificial bee colony algorithm for tracking multiple optima in uncertain environments (2014)
  16. Gao, Wei-feng; Liu, San-yang; Huang, Ling-ling: Enhancing artificial bee colony algorithm using more information-based search equations (2014)
  17. Garg, Harish: Solving structural engineering design optimization problems using an artificial bee colony algorithm (2014)
  18. Goudarzi, A.; Mozaffari, A.; Samadian, P.; Rezania, A.; Rosendahl, L.A.: Intelligent design of waste heat recovery systems using thermoelectric generators and optimization tools (2014)
  19. Han, Min; Liu, Chuang; Xing, Jun: An evolutionary membrane algorithm for global numerical optimization problems (2014)
  20. Liu, Hao; Ding, Guiyan; Wang, Bing: Bare-bones particle swarm optimization with disruption operator (2014)

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