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 299 articles )

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  1. Hu, Gang; Dou, Wanting; Wang, Xiaofeng; Abbas, Muhammad: An enhanced chimp optimization algorithm for optimal degree reduction of Said-Ball curves (2022)
  2. Kutlu Onay, Funda; Aydemir, Salih Berkan: Chaotic hunger games search optimization algorithm for global optimization and engineering problems (2022)
  3. Naz, Sidra; Raja, Muhammad Asif Zahoor; Kausar, Aneela; Zameer, Aneela; Mehmood, Ammara; Shoaib, Muhammad: Dynamics of nonlinear cantilever piezoelectric-mechanical system: an intelligent computational approach (2022)
  4. Örnek, Bülent Nafi; Aydemir, Salih Berkan; Düzenli, Timur; Özak, Bilal: A novel version of slime mould algorithm for global optimization and real world engineering problems. Enhanced slime mould algorithm (2022)
  5. Steakelum, Joshua; Aubertine, Jacob; Chen, Kenan; Nagaraju, Vidhyashree; Fiondella, Lance: Multi-phase algorithm design for accurate and efficient model fitting (2022)
  6. Zamani, Hoda; Nadimi-Shahraki, Mohammad H.; Gandomi, Amir H.: Starling murmuration optimizer: a novel bio-inspired algorithm for global and engineering optimization (2022)
  7. Avalos, Omar: GSA for machine learning problems: a comprehensive overview (2021)
  8. Chou, Jui-Sheng; Truong, Dinh-Nhat: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean (2021)
  9. Ibrahim, Rehab Ali; Abd Elaziz, Mohamed; Ewees, Ahmed A.; El-Abd, Mohammed; Lu, Songfeng: New feature selection paradigm based on hyper-heuristic technique (2021)
  10. Peng, Hu; Zhu, Wenhua; Deng, Changshou; Wu, Zhijian: Enhancing firefly algorithm with courtship learning (2021)
  11. Ren, Hao; Li, Jun; Chen, Huiling; Li, ChenYang: Adaptive Lévy-assisted salp swarm algorithm: analysis and optimization case studies (2021)
  12. Sahimi, Muhammad; Tahmasebi, Pejman: Reconstruction, optimization, and design of heterogeneous materials and media: basic principles, computational algorithms, and applications (2021)
  13. 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)
  14. Tavakoli, Masoud; Pourtaheri, Reza: Multivariate Bayesian control chart based on economic-statistical design with 2- and 3-variable sample size (2021)
  15. Tavakoli, Masoud; Pourtaheri, Reza: Multivariate Bayesian control chart based on economic-statistical design with 2 and 3-variable sample size (2021)
  16. Yang, Dahao; Lu, Zhong-Rong; Wang, Li: Parameter identification of bolted joint models by trust-region constrained sensitivity approach (2021)
  17. Yan, Zheping; Zhang, Jinzhong; Tang, Jialing: Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm (2021)
  18. Yan, Zheping; Zhang, Jinzhong; Zeng, Jia; Tang, Jialing: Nature-inspired approach: an enhanced whale optimization algorithm for global optimization (2021)
  19. Yu, Hang; Zhang, Yu; Cai, Pengxing; Yi, Junyan; Li, Sheng; Wang, Shi: Stochastic multiple chaotic local search-incorporated gradient-based optimizer (2021)
  20. 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)

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