A Non-hybrid Ant Colony Optimization Heuristic for Convergence Quality

Krynicki, Kamil; Houle, Michael E.; Jaén, Javier

doi:10.1109/smc.2015.300

Cited by 7 publications

(11 citation statements)

References 22 publications

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“…In chapter 6 we present a global solution to the semantic search in P2P networks with ACO. The paper, titled A non-Hybrid Ant Colony Optimization Heuristic for Convergence Quality [20] is the unveiling of our novel ACO algorithm called Angry Ant Framework (AAF ). AAF was initially designed to simply enable semantic searches with ACO, but it has proven to be better in non-semantic context as well, which is demonstrated in this paper.…”

Section: Contributionsmentioning

confidence: 99%

“…Angry Ant Framework (AAF) is formally introduced in [20], with the full mathematical explanation and the formal motivation. Here we present some of the main ideas of AAF with our recent improvements of the original model.…”

Section: Overviewmentioning

confidence: 99%

“…In our original proposal [20] of AAF we employ the irritation value as a probabilistic threshold. The probability p ( ) irr (r) is checked against a uniform random variable P upon an ant transitioning into a node r. If P > p ( ) irr (r), the ant ascends one pheromone level (7.12) and creates a new one, if needed:…”

Section: Ant Irritationmentioning

confidence: 99%

“…AAF algorithm has already proven its efficiency in the context of single-resource class queries [20], surpassing ACS, one of the most popular ACO 145 implementations. AAF improves the path quality of ACS by a significant margin, while at the same time decreasing the chance that the algorithm converges to a local minimum.…”

Section: Introductionmentioning

confidence: 99%

“…These disadvantages motivated the use of an alternative strategy, a middle ground solution between the single pheromone level and the static multilevel: the dynamic multilevel of AAF [20]. AAF was established as the first dynamic multilevel implementation of ACS that had the initial intention, like many of the aforementioned works, of improving the efficiency of single class queries.…”

Section: Introductionmentioning

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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Section: Contributionsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Ant Irritationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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