Swarming Models for Facilitating Collaborative Decisions

Zamfirescu, Constantin-Bala; Filip, Florin Gheorghe

doi:10.15837/ijccc.2010.1.2468

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…for group decision support systems ( [18]), stigmergic learning ( [19]), and many others (see [20]). …”

Section: Related Workmentioning

confidence: 99%

The Inn at the Crossroads – A Model of Distributed Spatial Knowledge

Șușnea¹

2016

SIC

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Abstract:The paper describes a simple experiment aimed to demonstrate the possibility to create distributed cognitive maps of certain environments by means of recording basic information about the purposeful motion of a population of mobile agents. The environment is assumed to consist in a set of behaviorally significant places interconnected by predefined paths, just like the inns at the crossroads used to be deployed along and across populated areas centuries ago. These active places interact with the mobile agents and create local patches of relevant navigation information, which is shared with other agents that reach the respective place. As a result, the system exhibits a globally consistent spatial behavior, using only locally available, incomplete spatial knowledge. A NetLogo simulation allowed us to compare the average distance traveled by the agents, and the average travel time before reaching their goals, with a similar system wherein the agents walk randomly. As expected, the agents using the distributed spatial knowledge reached their goals much faster than those walking randomly in the same environment. We also explore the possible applications of systems built according to the principles described here.

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“…for group decision support systems ( [18]), stigmergic learning ( [19]), and many others (see [20]). …”

Section: Related Workmentioning

confidence: 99%

The Inn at the Crossroads – A Model of Distributed Spatial Knowledge

Șușnea¹

2016

SIC

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“…The key concept of swarm intelligence lies in a simple set of rules that control each of the individuals which exhibit remarkable collective intelligence. The swarming concept can also be extended to human group decision process [6].…”

Section: Metaheuristicsmentioning

confidence: 99%

“…For each onlooker bee, produce a new solution υ i by Equation (6) in the neighborhood of the solution selected depending on p i and evaluate it 10. Apply selection process between υ i and x i based on Deb's method.…”

Section: Ga Operator Implementation For Abcmentioning

confidence: 99%

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Bačanin¹,

Tuba²

2012

SIC

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Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay's ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering mean solutions.

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“…• the effectiveness of these mechanisms depends on how the GDSS supports the integration of uncertainty associated with modeling and execution of GDP [14];…”

Section: B the Basic Architecture For The Stigmergic Frameworkmentioning

confidence: 99%

A Stigmergic Guiding System to Facilitate the Group Decision Process

Zamfirescu

Candea

2012

2012 IEEE 28th International Conference on Data Engineering Workshops

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The paper presents a stigmergic approach to engineer a guiding system to facilitate the complex problem of designing the group decision processes. The system aims to provide contextual, actionable recommendations based on the knowledge and past experience of its users as recorded in a collaborative working environment implemented around the concept of stigmergic systems. Through an agent-based socio-simulation experiment we have demonstrated already the feasibility of this approach. The paper illustrates how the simulation results are transferred into a guiding system that facilitates the group decision process design through iterative queries reformulations for the identification, representation and manipulation of the relevant knowledge.

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Swarming Models for Facilitating Collaborative Decisions

Cited by 13 publications

References 28 publications

The Inn at the Crossroads – A Model of Distributed Spatial Knowledge

The Inn at the Crossroads – A Model of Distributed Spatial Knowledge

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

A Stigmergic Guiding System to Facilitate the Group Decision Process

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