Multi Ant LA: An adaptive multi agent resource discovery for peer to peer grid systems

Olaifa, Moses; Mapayi, Temitope; Merwe, Ronell Van Der

doi:10.1109/sai.2015.7237180

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…For instance, the use of Turtlekit tool in Madkit platform [26] for simulating artificial life/reactive systems and the use of Repast platform for simulating social science applications [22]. It can illustrate, too, the use of bio-inspired Agent/multi-agent tools and models (Algorithms) such in [7,21,46,59,63].…”

Section: Beside Their Innate Characteristics (Section 21) Anmentioning

confidence: 99%

“…Some recent applications can be found, such as a parallel extended algorithm for the Ant Colony algorithm in [27] and Particle Swarm Optimization (PSO) algorithm in [37]. We can cite two others applications of the ACO Meta-heuristic for resource discovery in a grid using the agent technology [46] and for home automation networks [60]. A mobile agent Ant Algorithm (AA) has been used in an Ant-Based Cyber Defence system [21], when a hybrid Ant-Bee Algorithm was used for multirobot coverage in [7].…”

Section: Introductionmentioning

confidence: 99%

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Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Zeghida

Meslati

Bounour

2018

IJCAI

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Nowadays bio-inspired approaches are widely used. Some of them became paradigms in many domains, such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA). Despite the inherent challenges of surviving, in the natural world, biological organisms evolve, self-organize and self-repair with only local knowledge and without any centralized control. The analogy between biological systems and Multi-Agent Systems (MAS) is more than evident. In fact, every entity in real and natural systems is easily identified as an agent. Therefore, it will be more efficient to model them with agents. In a simulation context, MAS has been used to mimic behavioural, functional or structural features of biological systems. In a general context, bio-inspired systems are carried out with ad hoc design models or with a one target feature MAS model. Consequently, these works suffer from two weaknesses. The first is the use of dedicated models for restrictive purposes (such as academic projects). The second one is the lack of a design model. In this paper, our contribution aims to propose a generic multi-paradigms model for bio-inspired systems. This model is agent-based and will integrate different bio-inspired paradigms with respect of their concepts. We investigate to which extent is it possible to preserve the main characteristics of both natural and artificial systems. Therefore, we introduce the influence/reaction principle to deal with these bio-inspired multi-agent systems. Povzetek: Avtorji prispevka analizirajo podobnosti med biološkimi in multiagentnimi sistemi in predlagajo Bio-IR-M, integrirano shemo, ki zajema tako genetske algoritme kot npr. modele, temelječe na mravljah.

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Section: Beside Their Innate Characteristics (Section 21) Anmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Zeghida

Meslati

Bounour

2018

IJCAI

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“…The peer having the best behavior capacity was chosen as a provider. Olaifa et al introduced a resource discovery mechanism based on the learning automata and ant colony optimization techniques. The shortest paths within the grid system were located, and the optimal path was selected.…”

Section: Related Workmentioning

confidence: 99%

PSO‐based optimal peer selection approach for highly secure and trusted P2P system

Nallakannu¹,

Thiagarajan²

2016

Security Comm Networks

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There is a significant growth of the peer-to-peer (P2P) systems over the last few years because of their high potential for sharing file among the peers. Selection of the optimal peer ensures high data transmission rate and reduced network cost. However, it is highly difficult to select the optimal peer because of the variation in the heterogeneous capacity and dynamic capacity of the peers. Efficient peer selection approach is highly required to overcome this difficulty. This paper proposes a particle swarm optimization (PSO)-based optimal peer selection approach for a highly secure and trusted P2P system, based on the bandwidth and trust level of the peers. File replication is performed to effectively tackle the overloading of the hot files. Replacement of the replicated file is performed, based on their demand rates. The success rate and bandwidth rate of the peer are computed. Bandwidth and trust-based PSO is applied to select the optimal peer. This improves the success rate for the peer transmission. After selecting the optimal peer, the replicated file is encrypted and transmitted to the selected peer. Finally, the replicated file is decrypted. Our proposed approach achieves minimum average query delay and ensures a highly secure and trusted environment for the P2P systems. The performance of the proposed approach is evaluated by comparing the proposed approach with the existing techniques. From the comparison results, it is clearly evident that the proposed approach achieves higher success rate and lower link stress, delay, communication overhead, finished time and network cost.

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Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems

Verghelet

Mocskos

2017

Communications in Computer and Information Science

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Multi Ant LA: An adaptive multi agent resource discovery for peer to peer grid systems

Cited by 5 publications

References 17 publications

Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

PSO‐based optimal peer selection approach for highly secure and trusted P2P system

Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems

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