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. Hassan, Bryar A.; Rashid, Tarik A.: Operational framework for recent advances in backtracking search optimisation algorithm: a systematic review and performance evaluation (2020)
  2. Kutucu, Hakan; Gursoy, Arif; Kurt, Mehmet; Nuriyev, Urfat: The band collocation problem (2020)
  3. Liu, Jianjun; Zeng, Min; Ge, Yifan; Wu, Changzhi; Wang, Xiangyu: Improved cuckoo search algorithm for numerical function optimization (2020)
  4. Mostafaie, Taha; Modarres Khiyabani, Farzin; Navimipour, Nima Jafari: A systematic study on meta-heuristic approaches for solving the graph coloring problem (2020)
  5. Quaranta, Giuseppe; Lacarbonara, Walter; Masri, Sami F.: A review on computational intelligence for identification of nonlinear dynamical systems (2020)
  6. Wang, Shengliang; Liu, Genyou; Gao, Ming; Cao, Shilong; Guo, Aizhi; Wang, Jiachen: Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (2020)
  7. Yang, Yutao; Chen, Huiling; Li, Shimin; Heidari, Ali Asghar; Wang, Mingjing: Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers (2020)
  8. Yuan, Jinlong; Wu, Changzhi; Ye, Jianxiong; Xie, Jun: Robust identification of nonlinear state-dependent impulsive switched system with switching duration constraints (2020)
  9. Ziadi, Raouf; Bencherif-Madani, Abdelatif; Ellaia, Rachid: A deterministic method for continuous global optimization using a dense curve (2020)
  10. Bajer, Dražen; Zorić, Bruno: An effective refined artificial bee colony algorithm for numerical optimisation (2019)
  11. García Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Paredes-Sánchez, J. P.; Riesgo Fernández, P.: Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (2019)
  12. Ghiasi, N.; Khosravifard, A.: A novel method for estimation of intensity and location of multiple point heat sources based on strain measurement (2019)
  13. Li, S.; Trevelyan, J.; Wu, Z.; Lian, H.; Wang, D.; Zhang, W.: An adaptive SVD-Krylov reduced order model for surrogate based structural shape optimization through isogeometric boundary element method (2019)
  14. Mann, Palvinder Singh; Singh, Satvir: Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks (2019)
  15. Poo, Mark Ching-Pong; Yip, Tsz Leung: An optimization model for container inventory management (2019)
  16. Shan, Wenxuan; Yan, Qianqian; Chen, Chao; Zhang, Mengjie; Yao, Baozhen; Fu, Xuemei: Optimization of competitive facility location for chain stores (2019)
  17. Sun, Liling; Wu, Yuhan; Liang, Xiaodan; He, Maowei; Chen, Hanning: Constraint consensus based artificial bee colony algorithm for constrained optimization problems (2019)
  18. Tsai, Hsing-Chih: Confined teaching-learning-based optimization with variable search strategies for continuous optimization (2019)
  19. Wang, Yanjiao; Du, Tianlin: An improved squirrel search algorithm for global function optimization (2019)
  20. Yadav, Anupam; Anita; Kim, Joong Hoon: Convergence of gravitational search algorithm on linear and quadratic functions (2019)

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