Brain Storm Optimization Algorithm

By Yuhui Shi. International Conference on Swarm Intelligence, 2011

716 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/053346fbfa9630eb5a57822def5821d9d61b16b9

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

Citations in the corpus

Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, background): “A brain storm optimization algorithm enables a learning model’s high representation learning ability [52, 53], but it relies on an inner clustering process which can yield high computational complexity.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2018) 75 Beetle Swarm Optimization Algorithm (BSOA) Wang and Yang (2018) 76 Beluga Whale Optimization (BWO) Zhong et al.”
Metaheuristic research: a comprehensive survey (2018, methodology): “…Search Optimization Algorithm (Civicioglu 2013) BSO Bees Swarm Optimization (Djenouri et al. 2012) BSOA Brain Storm Optimization Algorithm (Shi 2011) CA Cultural Algorithm (Ali et al. 2016) CBM Coalition-Based Metaheuristic (Meignan et al. 2010) CBO Colliding Bodies Optimization (Kaveh…”
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, background): “2010 23 [135] Brain storm optimization Shi 2011 221 [136] Teaching–learning-based optimization Rao et al.”
Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning (2014, methodology): “Unlike traditional single-point based algorithms such as hill-climbing algorithms, a population-based swarm intelligence algorithm consists of a set of points (population) which solve the problem through information sharing to cooperate and/or compete among themselves (Shi, 2011a, b).”