Using Honey Bee Teamwork Strategy in Software Agents

Sadik, Sarmad; Ali, Arshad; Ahmad, Farooq; Suguri, Hiroki

doi:10.1109/cscwd.2006.253125

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Similarity between honey bee and agents teamwork inspired Sadik et al (2006) to develop a teamwork architecture to enhance the performance and task execution efficiency of software agents since a limited progress has been made towards efficient task execution mechanisms by group of agents in collaboration and coordination with each other. Sadik et al (2007) named it Honey Bee teamwork architecture afterwards.…”

Section: Task Allocationmentioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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Section: Task Allocationmentioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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“…ABC has a global search ability implemented through neighbourhood source production mechanism [75, 76]. In addition to ABC, many other new algorithms have been developed based on the cooperative behaviour of social honey bees [76–79]. For more details, we refer the reader to [78].…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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“…ABC has a global search ability implemented through neighbourhood source production mechanism [76,77]. In addition to ABC, many other new algorithms have been developed based on the cooperative behaviour of social honey bees [77,[80][81][82]. For more details, we refer the reader to [81].…”

Section: Bee Colony Optimization-based Algorithmsmentioning

confidence: 99%

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

Latah,

Toker

2018

Preprint

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Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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Using Honey Bee Teamwork Strategy in Software Agents

Cited by 5 publications

References 2 publications

A survey: algorithms simulating bee swarm intelligence

A survey: algorithms simulating bee swarm intelligence

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

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