Evolutionary population dynamics and grey wolf optimizer
By Shahrzad Saremi, Seyedeh Zahra Mirjalili, Seyedali Mirjalili. Neural computing & applications (Print), 2014
354 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/5592e90b7a3997c2a4dba45e023c8460db984335
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ali Asghar Heidari
- Gai-ge Wang
- Leandro dos S. Coelho
- Parham Pahlavani
- Yongquan Zhou
- Qifang Luo
- Ying-Biao Ling
- Mohammad-Hossein Nadimi-Shahraki
In Computational Theory and Mathematics
- Ali Asghar Heidari
- Mohammad-Hossein Nadimi-Shahraki
- Leandro dos S. Coelho
- Parham Pahlavani
- S. Mirjalili
- Qifang Luo
- Yongquan Zhou
- Suash Deb
Citations in the corpus
- An improved grey wolf optimizer for solving engineering problems (2021, methodology): “Saremi et al. (Saremi et al., 2015), proposed GWO-EPD by integrating the evolutionary population dynamics (EPD) operator and the canonical GWO algorithm.”
- Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization (2017, background): “SIMULATION EXPERIMENTS There are various benchmark functions in the literature [24]–[27] that are designed to benchmark the performance of metaheuristic methods.”
- An efficient modified grey wolf optimizer with Lévy flight for optimization tasks (2017, background): “These stochastic optimizers have independently applied in the different field of sciences, which may be due to their metamorphoses in the source of inspiration, exploration and exploitation mechanisms, convergence characteristics, and the optimality of the results [2-7].”
- Monarch butterfly optimization (2015, neutral): “Inspired by this phenomenon, the ACO algorithm [11] is proposed by Dorigo et al. Recently, more superior SI algorithms have been proposed, such as artificial bee colony (ABC) [13, 14], cuckoo search (CS) [15– 19], bat algorithm (BA) [20–22], grey wolf optimizer (GWO) [23, 24], ant lion optimizer (AL”
- Elephant Herding Optimization (2015, background): “…charged system search (CSS) [35-37], chaotic swarming of particles (CSP) [38], monarch butterfly optimization (MBO) [39], krill herd (KH) [40-47], multi-verse optimizer (MVO) [48], dragonfly algorithm (DA) [49], grey wolf optimizer (GWO) [50, 51], wolf search algorithm (WSA) [52], among others.”