Swarm Intelligence: From Natural to Artificial Systems

By B. Webb. 2002

3,317 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Statistical and Nonlinear Physics

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In Biomedical Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Deep Reinforcement Learning for Swarm Systems (2018, background): “Swarm systems have been used for a variety of applications, including foraging, formation control, collective manipulation, or the localization of a common ’food’ source [Bonabeau et al., 1999].”
Collective cognition in animal groups. (2009, methodology): “Furthermore, evolved coordinated behavior serves as direct inspiration for designed systems such as autonomous robot groups and computational search algorithms [ 67 ].”
Ant-based and swarm-based clustering (2007, methodology): “(Reynolds 1987), which is imitated in collaborative robotics (Bonabeau et al. 1999) and”
Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors (2002, neutral): “nutrient concentration located at [15, 5].”
A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, background): “It refers to the collective intelligence that emerges from the cooperation of swarm agents [210].”
Everything you need to know about agent-based modelling and simulation (2016, methodology): “Swarm optimization, from the ﬁ eld of bio-inspired evolutionary algorithms (Olariu and Zomaya, 2006), has been used to solve practical, large-scale optimization problems (Bonabeau et al , 1999; Barbati et al , 2011).”
A comparative study of Artificial Bee Colony algorithm (2009, background): “Bonabeau has defined the swarm intelligence as ''. . .any attempt to design algorithms or distributed problem-solving devices inspired by the collective behaviour of social insect colonies and other animal societies. . . " [11].”
Swarm robotics: a review from the swarm engineering perspective (2013, background): “The interest towards social animals stems from the fact that they exhibit a sort of swarm intelligence (Bonabeau et al. 1999; Dorigo and Birattari 2007).”
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “Another feature of SI is this indirect communication by modification of the environment, also referred to as stigmergy [18].”
Artificial bee colony algorithm (2010, background): ““any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” [1]”
Self-assemblages in insect societies (2002, background): “…remarkable examples of sophisticated collective activity including nest construction, complex foraging strategies, and collective decision making (e.g. Deneubourg and Goss, 1989; Hölldobler and Wilson, 1990; Seeley, 1995; Bonabeau et al., 1999; Anderson and Franks, 2001; Camazine et al., 2001).”
Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions (2011, background): “Swarm intelligence (SI) [9,27,47] is a relatively novel field that was originally defined as ‘‘Any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insects and other animal societies” [9].”
Embodied Artificial Intelligence: Trends and Challenges (2003, background): “Work on self-organization in insect societies, for example, by Jean-Louis Deneubourg of the Université libre de Bruxelles, has attracted many researchers from different fields: “ant intelligence” was one of their slogans (e.g. Bonabeau et al., 1999).”
How the body shapes the way we think - a new view on intelligence (2006, background): “, 2002) and “swarm intelligence” (Bonabeau et al., 1999) are among their coinages (see also Dorigo and Stützle, 2004).”
Firefly Algorithm, Lévy Flights and Global Optimization (2010, neutral): “Nature-inspired metaheuristic algorithms are becoming powerful in solving modern global optimization problems [2, 3, 5, 7, 9, 18, 17], especially for the NP-hard optimization such as the travelling salesman problem.”
Pheromone Robotics (2001, methodology): “We are inspired by techniques used by ants and termites for communication and coordination (Bonabeau et al., 1999; Goss et al., 1990).”
Self-organized structures in a superorganism: do ants "behave" like molecules? (2006, neutral): “[5,13,20,77,88,89,94,119]).”