Krill herd

Krill herd: A new bio-inspired optimization algorithm. A novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.


References in zbMATH (referenced in 53 articles , 1 standard article )

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  1. Alimoradi, Mahmoud; Azgomi, Hossein; Asghari, Ali: Trees social relations optimization algorithm: a new swarm-based metaheuristic technique to solve continuous and discrete optimization problems (2022)
  2. Das, Subhajit; Mondal, Rajan; Shaikh, Ali Akbar; Bhunia, Asoke Kumar: An application of control theory for imperfect production problem with carbon emission investment policy in interval environment (2022)
  3. Abualigah, Laith; Diabat, Ali; Mirjalili, Seyedali; Abd Elaziz, Mohamed; Gandomi, Amir H.: The arithmetic optimization algorithm (2021)
  4. Hodashinsky, I. A.: Methods for improving the efficiency of swarm optimization algorithms. A survey (2021)
  5. Ngo, Van-Quang-Binh; Latifi, Mohsen; Abbassi, Rabeh; Jerbi, Houssem; Ohshima, Kentaro; khaksar, Mehrdad: Improved krill herd algorithm based sliding mode MPPT controller for variable step size P&O method in PV system under simultaneous change of irradiance and temperature (2021)
  6. Shekhawat, Shalini; Saxena, Akash; Kumar, Rajesh; Singh, Vinay Pratap: Levy flight opposition embedded BAT algorithm for model order reduction (2021)
  7. Smejkal, Tomáš; Mikyška, Jiří; Kukal, Jaromír: Comparison of modern heuristics on solving the phase stability testing problem (2021)
  8. He, Hengjing; Zhou, Shangli; Zhang, Leping; Lin, Junhong; Chen, Weile; Wu, Di: Beetle swarm optimization algorithm-based load control with electricity storage (2020)
  9. Peng, Yuexi; Sun, Kehui; He, Shaobo: An improved return maps method for parameter estimation of chaotic systems (2020)
  10. Salgotra, Rohit; Gandomi, Mostafa; Gandomi, Amir H.: Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries (2020)
  11. Wachs-Lopes, G. A.; Santos, R. M.; Saito, N. T.; Rodrigues, P. S.: Recent nature-inspired algorithms for medical image segmentation based on Tsallis statistics (2020)
  12. Zhang, Xinming; Wang, Doudou; Fu, Zihao; Liu, Shangwang; Mao, Wentao; Liu, Guoqi; Jiang, Yun; Li, Shuangqian: Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation (2020)
  13. Zhang, Xuncai; Zhao, Kai: An improved squirrel search algorithm with reproduction and competition mechanisms (2020)
  14. Zhang, Yin; Wang, Gai-Ge; Li, Keqin; Yeh, Wei-Chang; Jian, Muwei; Dong, Junyu: Enhancing MOEA/D with information feedback models for large-scale many-objective optimization (2020)
  15. Babayan, Narek; Tahani, Mojtaba: Team arrangement heuristic algorithm (TAHA): theory and application (2019)
  16. Da Silva Fernandes, Filipa; Stasinakis, Charalampos; Zekaite, Zivile: Forecasting government bond spreads with heuristic models: evidence from the eurozone periphery (2019)
  17. Gaiardo Fossati, Giovani; Fleck Fadel Miguel, Letícia; Paucar Casas, Walter Jesus: Multi-objective optimization of the suspension system parameters of a full vehicle model (2019)
  18. Huang, Qiujun; Zhang, Kai; Song, Jinchun; Zhang, Yimin; Shi, Jia: Adaptive differential evolution with a Lagrange interpolation argument algorithm (2019)
  19. Mashwani, Wali Khan; Zaib, Alam; Yeniay, Özgür; Shah, Habib; Tairan, Naseer Mansoor; Sulaiman, Muhammad: Hybrid constrained evolutionary algorithm for numerical optimization problems (2019)
  20. Wei, Lixin; Li, Xin; Fan, Rui: A new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism (2019)

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