Detection of multiple source locations using a glowworm metaphor with applications to collective robotics

By Krishnanand N. Kaipa, Debasish Ghose. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005

369 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Insect Science

View Paper: https://www.semanticscholar.org/paper/07a838bec281e77986096024db7defd80035c248

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Citations in the corpus

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, neutral): “Krishnanand and Ghose (2005) 20.”
Bio inspired computing - A review of algorithms and scope of applications (2016, background): “, 2009; Yang & Deb, 2010), individual insect based algorithms like fruit fly, wasp and glow-worm (Pan, 2012; Theraulaz et al. 1991; Krishnanand and Ghose 2005), individual animal based algorithms like monkey, shark, wolf and lions (Mucherino, and Seref, 2007; Hersovici et al.”
New inspirations in swarm intelligence: a survey (2011, methodology): “The GSO algorithm was first presented by Krishnanand and Ghose (2005) as an application to collective robotics.”
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (2009, methodology): “Preliminary results obtained with GSO were presented in (Krishnanand and Ghose 2005, 2006a).”
Red deer algorithm (RDA): a new nature-inspired meta-heuristic (2020, background): “…improvising process of composing a piece of music (Geem et al. 2001), glow-worm swarm optimization (GSO) based on the ﬂashing behavior of glow-worms (Krishnanand and Ghose 2005), imperialist competitive algorithm (ICA) based on imperialistic competition (Atashpaz-Gargari and Lucas 2007) and…”
A social spider algorithm for global optimization (2015, background): “Krishnanand and Ghose proposed a Glow-worm Swarm Optimization (GSO) [21] based on the behavior of the ﬁreﬂy.”
Metaheuristics - the metaphor exposed (2015, methodology): “…algorithms were introduced involving ants, honey bees (Karaboga, 2005), flies (Abidin et al., 2010), fruit flies (Pan, 2011), termites (Hedayatzadeh et al., 2010), fireflies (Łukasik and Żak, 2009), glow worms (Krishnanand and Ghose, 2005), and probably some other that the author is unaware of.”
A Comprehensive Review of Swarm Optimization Algorithms (2015, methodology): “GSO has been applied to routing [94], swarm robotics [95], image processing [96], and localization [97, 98] problems.”
A Brief Review of Nature-Inspired Algorithms for Optimization (2013, neutral): “ool Li et al. [39] Queen-bee evolution Jung [30] Good lattice swarm optimization Su et al. [58] Shufﬂed frog leaping algorithm Eusuff and Lansey [18] Glowworm swarm optimization Krishnanand and Ghose [37], [38] Termite colony optimization Hedayatzadeh et al. [27] Hierarchical swarm model Chen et al.”
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, methodology): “2005 157 [37] Glowworm swarm optimization Krishnanand and Ghose 2005 342 [38] Honey bee swarm optimization algorithm Karaboga 2005 5009 [39] Virtual bee algorithm Yang 2005 349 [40] Bees algorithms Pham et al.”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “FSA Fish-Swarm Algorithm [138] FSS Fish School Search [139] GEA Group Escaping Algorithm [140] GOA Grasshopper Optimisation Algorithm [141] GSO Glow-worm Swarm Optimization [142] GWO Grey Wolf Optimizer [143] HBA Honey Bee Algorithm [144] HBF Honey Bee Foraging [145] HBMO Honey Bees Mating Optimizat”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, methodology): “Krishnanand and Ghose [11] proposed glowworm swarm optimization (GSO) method, the agents in which are thought of as glow-”