Collective Self-Tuning for Complex Product Design

Kaddoum, Elsy; Georgé, Jean‐Pierre

doi:10.1109/saso.2012.14

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…2. The amount of the food source, namely the fitness value of each of the food sources, is obtained via equation (2) or (3) of the fitness function:…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…Complex systems comprise all the components involved in a given interaction, each of which necessitates an advanced level of information processing and each of which tends to respond to changes in the environment. As it is difficult to analyze complex systems in the real environment, agent-based modeling and simulation techniques are used instead [1][2][3]. Agent-based modeling and simulation are used as a design tool for analysis and newly installed systems and also to estimate the real system criteria [4].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Tan¹,

Bora²

2020

Turk J Elec Eng & Comp Sci

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Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields.

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“…2. The amount of the food source, namely the fitness value of each of the food sources, is obtained via equation (2) or (3) of the fitness function:…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Tan¹,

Bora²

2020

Turk J Elec Eng & Comp Sci

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“…Besides, until know, AVT approach has been successfully used in several projects such as maritime surveillance [42], [43], scenario control in games [44], self-organizing neural networks [45], [46], manufacturing control [47], user profiling [48]. Moreover, its use by engineers of UPETEC Ltd 13 in various industrial projects have shown good results.…”

Section: Related Workmentioning

confidence: 99%

Efficient Time Synchronization in a Wireless Sensor Network by Adaptive Value Tracking

Yıldırım

Gürcan

2014

IEEE Trans. Wireless Commun.

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“…More precisely, MAS considered here are adaptive ones and developed based on the Adaptive Multi-Agent Systems (AMAS) theory (Capera et al 2003). The AMAS theory has successfully been applied to various application domains such as ambient systems (Guivarch et al 2012), manufacturing control (Kaddoum and Georgé 2012), maritime surveillance (Brax et al 2012), optimization (Combettes et al 2012), bioprocesses control (Videau et al 2011), crisis management (Lacouture et al 2011), user profiling (Lemouzy et al 2010, simulation of the functional behavior of a yeast cell (Bernon et al 2009), aircraft/avionics design (Welcomme et al 2009), dynamic ontologies (Ottens et al 2007) and aeronautical mechanisms design (Capera et al 2004).…”

Section: Artificial Self-organizationmentioning

confidence: 99%

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

Gürcan

Türker

Mano³

et al. 2013

J Comput Neurosci

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We present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments.

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Collective Self-Tuning for Complex Product Design

Cited by 5 publications

References 7 publications

Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Efficient Time Synchronization in a Wireless Sensor Network by Adaptive Value Tracking

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

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