Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

By Shang He, Q. Henry Wu, Jon R. Saunders. IEEE Transactions on Evolutionary Computation, 2009

714 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Application of binary group search optimization to distribution network reconfiguration (2014, methodology): “This theory mainly based on the producer–scrounger (PS) model and the animals scanning mechanisms are employed metaphorically for designing an optimum searching strategy for solving the optimization problems [21].”
The Social Engineering Optimizer (SEO) (2018, neutral): “Related researchers have studied on these functions to show the performance of novel meta-heuristics (Karaboga and Akay, 2009; He et al., 2009; Rao and Patel, 2013; Ghorbani and Babaei, 2014).”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, methodology): “Lastly, we pit the HMRPSO against the FSDE [42], the GLRCGA [43], and the GSO [44].”
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm (2016, methodology): “EAs differ from some optimization methods, such as Simulated-Annealing [2] and Tabu search [3], because they evolve a population of solutions to reach an approximate optimal solution instead of one solution [4].”