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. Kutlu Onay, Funda; Aydemir, Salih Berkan: Chaotic hunger games search optimization algorithm for global optimization and engineering problems (2022)
  2. Avalos, Omar: GSA for machine learning problems: a comprehensive overview (2021)
  3. Chou, Jui-Sheng; Truong, Dinh-Nhat: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean (2021)
  4. Ibrahim, Rehab Ali; Abd Elaziz, Mohamed; Ewees, Ahmed A.; El-Abd, Mohammed; Lu, Songfeng: New feature selection paradigm based on hyper-heuristic technique (2021)
  5. Peng, Hu; Zhu, Wenhua; Deng, Changshou; Wu, Zhijian: Enhancing firefly algorithm with courtship learning (2021)
  6. Ren, Hao; Li, Jun; Chen, Huiling; Li, ChenYang: Adaptive Lévy-assisted salp swarm algorithm: analysis and optimization case studies (2021)
  7. Sun, Pu; Liu, Hao; Zhang, Yong; Tu, Liangping; Meng, Qingyao: An intensify atom search optimization for engineering design problems (2021)
  8. Su, Zhi-gang; Zhou, Hong-yu; Hao, Yong-sheng: Evidential evolving C-means clustering method based on artificial bee colony algorithm with variable strings and interactive evaluation mode (2021)
  9. Tavakoli, Masoud; Pourtaheri, Reza: Multivariate Bayesian control chart based on economic-statistical design with 2 and 3-variable sample size (2021)
  10. Yang, Dahao; Lu, Zhong-Rong; Wang, Li: Parameter identification of bolted joint models by trust-region constrained sensitivity approach (2021)
  11. Yan, Zheping; Zhang, Jinzhong; Tang, Jialing: Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm (2021)
  12. Yan, Zheping; Zhang, Jinzhong; Zeng, Jia; Tang, Jialing: Nature-inspired approach: an enhanced whale optimization algorithm for global optimization (2021)
  13. Zhang, Jianan; Chu, Liang; Wang, Xu; Guo, Chong; Fu, Zicheng; Zhao, Di: Optimal energy management strategy for plug-in hybrid electric vehicles based on a combined clustering analysis (2021)
  14. Zhang, Sen; Zhou, Guo; Zhou, Yongquan; Luo, Qifang: Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization (2021)
  15. Zhou, Xinyu; Lu, Jiaxin; Huang, Junhong; Zhong, Maosheng; Wang, Mingwen: Enhancing artificial bee colony algorithm with multi-elite guidance (2021)
  16. Ahmadianfar, Iman; Bozorg-Haddad, Omid; Chu, Xuefeng: Gradient-based optimizer: a new metaheuristic optimization algorithm (2020)
  17. Akay, Rustu; Akay, Bahriye: Artificial bee colony algorithm and an application to software defect prediction (2020)
  18. Elaziz, Mohamed Abd; Ewees, Ahmed A.; Ibrahim, Rehab Ali; Lu, Songfeng: Opposition-based moth-flame optimization improved by differential evolution for feature selection (2020)
  19. Ghoshal, Sudishna; Sundar, Shyam: Two heuristics for the rainbow spanning forest problem (2020)
  20. Giladi, Chen; Sintov, Avishai: Manifold learning for efficient gravitational search algorithm (2020)

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