Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives

Trianni, Vito; Tuci, Elio; Passino, Kevin K.M.; Marshall, James A. R.

doi:10.1007/s11721-010-0050-8

Cited by 76 publications

(56 citation statements)

References 36 publications

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“…En esta línea de ideas, este trabajo constituye una aproximación empírica que retoma los conceptos planteados por Trianni et al (2011) y Krause et al (2011. Se espera que el naciente campo de la cognición de enjambre encuentre un nicho investigativo mediante la exploración de su potencial en la comprensión de las interacciones grupales y las complejas interacciones entre conglomerados de neuronas en el cerebro de los vertebrados.…”

Section: Discussionunclassified

Cognición de grupo y cognición de enjambre en la solución del problema del agente viajero

Flórez

2014

Rev. Guillermo Ockham

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Referencia para citar este artículo: Martínez-Flórez, J. F. (2014). Cognición de grupo y cognición de enjambre en la solución del problema del agente viajero. Revista Científica Guillermo de Ockham, 12(1),[9][10][11][12][13][14][15][16][17][18] Resumen En este trabajo se explora la solución colectiva de problemas mediante la comparación del desempeño grupal humano y los algoritmos de inteligencia de enjambres. En el estudio participaron treinta estudiantes universitarios de distintos programas académicos, con edades entre dieciocho y treinta años que fueron asignados en grupos conformados por tres, cinco o siete integrantes. Se utilizó una versión espacial (campo abierto) del problema del agente viajero. El desempeño de los participantes se comparó con la ejecución de algoritmos de inteligencia colectiva de enjambres. Los resultados muestran algunas semejanzas entre la cognición de grupo y la cognición de enjambre en los mecanismos de procesamiento de información, las estrategias de organización colectiva y las pautas de elección o estrategias de consenso.Palabras clave: Cognición grupal, consenso, cognición de enjambre, resolución colectiva problemas.Group cognition and swarm cognition in solving the traveling salesman problem AbstractIn this paper collective problem solving is explored by comparing the human group performance and swarm intelligence algorithms. The study involved thirty university students from different academic programs, aged between eighteen and thirty years were assigned comprised of three, five or seven-member groups. Spatial version (open field) of the traveling salesman problem was used. The participants' performance was compared with the performance of algorithms collective intelligence of swarms. The results show some similarities between cognition and group cognition swarm in the mechanisms of information processing strategies of collective organization and patterns of choice or consensus strategies.

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Section: Discussionunclassified

Cognición de grupo y cognición de enjambre en la solución del problema del agente viajero

Flórez

2014

Rev. Guillermo Ockham

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“…In self-organizing communities each individual either a insect or a neuron acts according to the information it receives from its local community, without having any global representation, but by following simple individual rules. multiple local communication and the adaptive behavior of bees which adjust their responses according to their neighborhood environments [8]. This adaptation of honeybees is similar to the adaptation made by neurons when learning occurs at human brain.…”

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confidence: 80%

Modeling Function of Nectar Foraging of Honeybees using Operant Conditioning

Fernando¹

2014

IJCA

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Behaviors of the colonies of small unsophisticated agents have been analyzed in the literature with the purpose of developing efficient algorithms to solve complex, dynamic and burden problems in other societies. Among them, only a few research have been conducted in the area of swarm cognition which tries to understand the cognitive behaviors exhibited by human brain by using the cognitive behaviors demonstrated by a colony as a self-organized entity. In this aspect, the role of a neuron and a role of a insect have been equally considered as an unsophisticated agent which adjusts its actions according to the fluctuations of local environment without knowing any global information. The cognitive behavior, such as effective labor division of honeybees at food foraging process, was analyzed in this paper and has been exploited under operant conditioning. The paper has proposed a simple but effective computational model which demonstrates that, the positive reinforcement and the negative reinforcement in operant conditioning are the real factors that affect to the emergent of cognitive behaviors at swarm level when swarm is observed as a self-organized entity.

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“…The differential search algorithm (DSA) is the latest addition to this group. Movement of a superorganism can be described by a Brownian-like random walk model (Trianni et al, 2011). In DSA, it is assumed that a population made up of random solutions of the respective problem corresponds to an artificial superorganism migration (Civicioglu, 2012).…”

Section: Differential Search Algorithmmentioning

confidence: 99%

“…The mechanism of finding a stop-over site involves implementation of the Brownian-like random walk model (Trianni et al, 2011). The randomly selected individuals of the artificial organisms move towards the targets of donor = [X Random_Shuffling(i) ] in order to discover these stop-over sites, which are pivotal for a successful migration.…”

Section: : End Formentioning

confidence: 99%

Differential search algorithm-based parametric optimization of electrochemical micromachining processes

Goswami¹,

Chakraborty²

2013

10.5267/j.ijiec

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Electrochemical micromachining (EMM) appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nanoelectromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer's desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA) for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.

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Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives

Cited by 76 publications

References 36 publications

Cognición de grupo y cognición de enjambre en la solución del problema del agente viajero

Cognición de grupo y cognición de enjambre en la solución del problema del agente viajero

Modeling Function of Nectar Foraging of Honeybees using Operant Conditioning

Differential search algorithm-based parametric optimization of electrochemical micromachining processes

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