A diffusion-based ACO resource discovery framework for dynamic p2p networks

Krynicki, Kamil; Jaén, Javier; Catalá, Alejandro

doi:10.1109/cec.2013.6557658

Cited by 5 publications

(5 citation statements)

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“…After having defined and demonstrated the problem, a proposal of a P2P-compatible ACO was deployed as an experimental module, formulated completely within AntE [7]. We opted for counteracting the problems associated to the dynamism of the graph by correcting the pheromone paths with the help of a new type of ants, the graph structure diffusion ants.…”

Section: Existing Implementationsmentioning

confidence: 99%

AntElements

Krynicki

Jaén

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Ant Colony Optimization (ACO) has become a popular metaheuristic approach for solving hard combinatorial optimization problems. However, most existing ACO software systems are domain-specific, dedicated to concrete problems or non-extensible, non-portable and non-scalable solutions that have been evaluated for problem spaces of limited size. In this context, we present AntElements (AntE), a portable Java-based ACO middleware, designed and implemented with the highest consideration for versatility. The extensibility of the proposed middleware allows its use in virtually any ACO deployment, ranging from experimental to commercial. In this work, the overall object-oriented architecture and the software design patterns of AntE are explained, alongside the main concepts behind them. Furthermore, AntE is analyzed with respect to the computational efficiency, parallelization capacities and memory consumption, which allows to establish its usability and scalability range. In its current implementation, on an average to mid-high workstation, our middleware is capable of processing upwards of 10 5 agents per second, in graphs of the order of 10 5 nodes and sustain a stable, fully logged experiment for over 12 hours. The proposed middleware has already been deployed in several research projects that are outlined in this paper, illustrating the range of possibilities it offers.

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Section: Existing Implementationsmentioning

confidence: 99%

AntElements

Krynicki

Jaén

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…To prevent over-exploitation and encourage exploration, these pheromones are subject to evaporation, causing the attractive strength of a given pathway to decrease over time (Dorigo et al, 2006). In addition, pheromones can also be subject to diffusion, allowing agents to explore adjacent solutions (Ji et al, 2008;Krynicki et al, 2013). As previously mentioned in Section 3.1.2, Ant Colony algorithms can also be used in systems comprised of two different levels of pheromone sensitivity (Chira et al, 2008;Pintea et al, 2009).…”

Section: Stigmergymentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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“…Even though it has been stated on several occasions ( [18] [31]) it still is interesting to see that reaching deg(v) < 3.00 does not impair the HpH value much. RC-ACS in the network with deg(v) = 0.78, which indicates isolated nodes, is better than SemAnt with deg(v) = 4.…”

Section: Experiments 2: Resgrow 10kmentioning

confidence: 96%

“…Having formalized and demonstrated the problem we proceed to our first complete solution to the problem of dynamism. In chapter 4, in the paper A Diffusion-Based ACO Resource Discovery Framework for Dynamic P2P Networks [18], we show how to efficiently combat the network dynamism by a new mechanism called information diffusion, fully within the ACO metaheuristic. 4.…”

Section: Contributionsmentioning

confidence: 99%

“…After having defined and demonstrated the problem, a proposal of a P2P-compatible ACO was deployed as an experimental module, formulated completely within AntE [18]. We opted for counteracting the problems associated to the dynamism of the graph by correcting the pheromone paths with the help of a new type of ants, the graph structure diffusion ants.…”

Section: Existing Implementationsmentioning

confidence: 99%

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Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Krynicki¹

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SummaryThe long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P ) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing.In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergybased, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency.In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS ) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems.First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between iii search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS ). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution.Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF ). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassi...

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A diffusion-based ACO resource discovery framework for dynamic p2p networks

Cited by 5 publications

References 19 publications

AntElements

AntElements

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

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