Panama Geer, Harry W. Mclaughlin, Keith Unsworth et al. · 2001
Management and Computing Division at Lincoln University whose research and teaching interests are in computing and quantitative disciplines. Previously this group was the academic section of the Centre for Computing and Biometrics at Lincoln University. The group teaches subjects leading to a Bachelor of Applied Computing degree and a computing major in the Bachelor of Commerce and Management. In addition, it contributes computing, statistics and mathematics subjects to a wide range of other Lincoln University degrees. In particular students can take a computing and mathematics major in the BSc. The ACMS group is strongly involved in postgraduate teaching leading to honours, masters and PhD degrees. Research interests are in modelling and simulation, applied statistics, end user computing, computer assisted learning, aspects of computer networking, geometric modelling and visualisation. Research Reports Every paper appearing in this series has undergone editorial review within the ACMS group. The editorial panel is selected by an editor who is appointed by the Chair of the Applied Management and Computing Division Research Committee. The views expressed in this paper are not necessarily the same as those held by members of the editorial panel. The accuracy of the information presented in this paper is the sole responsibility of the authors. This series is a continuation of the series "Centre for Computing and Biometrics Research Report " ISSN 1173-8405. Copyright Copyright remains with the authors. Unless otherwise stated permission to copy for research or teaching purposes is granted on the condition that the authors and the series are given due acknowledgement. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors.
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Marco Dorigo, Gianni A. Di, Thomas Stützle · Future Generation Computer Systems · 2000
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2006
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Yu‐Chiun Chiou, Shih-Ta Chou · International Journal of Applied Evolutionary Computation · 2010
This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.
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Janson, S., Merkle, D., Middendorf, M. · Publikation Server (Leipzig University)
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Válek, Matěj · Brno University of Technology Digital Library (Brno University of Technology) · 2018
Tato bakalářská práce se zabývá různými aplikacemi optimalizací mravenčí kolonií. Zejména algoritmus ant colony system bude použit pro optimalizaci problému obchodního cestujícího a návrh pravidel pro vývoj celulárních automatů. Výsledky budou statisticky analyzovány. Kromě toho byla vytvořena GUI aplikace, která umožňuje interaktivně sledovat vývoj algoritmu ant colony system pro vzdělávací účely.
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Stefan Janson, Daniel Merkle, Martin Middendorf · 2005
This chapter contains sections titled: Introduction Ant Colony Optimization Parallel ACO Hardware Parallelization of ACO Other Ant Colony Approaches References
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T. R. Stickland, Chris Tofts, Nigel R. Franks · Die Naturwissenschaften · 1993
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Middendorf, M., Schmeck, H., Reischle, F. · Publikation Server (Leipzig University) · 2002
In multi colony ant algorithms several colonies of ants cooperate in finding good solutions for an optimization problem. At certain time steps the colonies exchange information about good solutions. If the amount of exchanged information is not too large multi colony ant algorithms can be easily parallelized in a natural way by placing the colonies on different processors. In this paper we study the behaviour of multi colony ant algorithms with different kinds of information exchange between the colonies. Moreover we compare the behaviour of different numbers of colonies with a multi start single colony ant algorithm. As test problems we use the Traveling Salesperson problem and the Quadratic Assignment problem.
Cited by 6Open access
And Fuzzy Unl · 2003
In the context on the UNL project, we focus on the automatization of enconversion process, that is the building of UNL graphs from sentences. We present an extension of the UNL graph structure aiming at handling lexical and relational ambiguities. On this intermediate structure, we can apply ant algorithm propagation of conceptual vectors and other constraints. Graph nodes and relations have a level of excitement and when this level remains too low for too long they are deleted. This way, both acception and attachment selections can be performed.
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Kaščák, Imrich · Brno University of Technology Digital Library (Brno University of Technology) · 2018
Práca sa zaoberá základným optimalizačným algoritmom Ant Colony Optimization (ACO) - Ant System (AS) a jeho rozšírením Ant Colony System (ACS) na probléme obchodného cestujúceho (TSP). Podstatou týchto algoritmov je nájdenie optimálneho riešenia (najkratšej cesty) v zadanej inštancii obsahujúcej niekoľko miest. Demonštruje overenie fungovania oboch algoritmov, experimentálne skúmanie vplyvu nastaviteľných parametrov mravčích algoritmov na výsledok. Ďalej je práca zameraná na skúmanie myšlienky optimalizácie detekcie hrán v obrazoch zavedením modifikácie do predstaveného existujúceho riešenia. Experimenty modifikovaného riešenia sú vykonané a porovnané s pôvodným.
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Roli, Andrea · AMS Campus - AlmaDL · 2018
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Nicolas Monmarché · HAL (Le Centre pour la Communication Scientifique Directe) · 2010
International audience
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2008
In the previous chapter we looked at a number of examples of distributed systems. Among others, we saw that ants are capable of finding the shortest path to a food source via a process of self-organization, mediated through pheromone trails (chapter 3. section 3.2). Through such stigmergic interactions 1, i.e. interactions mediated by modifications of the environment (depositing pheromones), they solve complex optimization problems. In addition to solving these problems, their behavior is robust, i.e. the “solution ” is immune to noise. The ants also remain adaptive, i.e. if the environmental situation changes, for example a path is no longer, or a shortcut becomes available, they find different solutions. These characteristics have lead to the development of artificial systems, which were inspired by the observation of colonies of social insects in nature. These systems consist of several agents (e.g. artificial ants) with simple basic capabilities, which lead to a highly complex structure of the whole system as resulting emergent behavior. 4.1 Ant Based Control The remarkable properties of ants have encouraged researchers at Hewlett-Packard and British Telecom to try and apply these concepts to the problem of load balancing and message routing in telecommunications networks. Their network model is populated by agents (artificial ants) that make use of the trail-laying principles, i.e. they deposit pheromone on each node they pass during their trip through the network. The
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Serhiy Shtovba · Programming and Computer Software · 2005
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Jürgen Branke, Michael Decker, Daniel Merkle et al. · Lecture notes in computer science · 2002
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Selection Of The, Marco Dorigo, Gianni Di Caro · 2004
Introduction Colonies of social insects can exhibit an amazing variety of complex behaviors and have captured, since ever, the interest of biologists and entomologists. More recently, computer scientists have found in the study of social insects behavior a source of inspiration for the design and implementation of novel distributed multi-agent algorithms. In particular, the study of ant colonies behavior turned out to be very fruitful, giving raise to a completely novel eld of research, now known as ant algorithms. In ant algorithms a colony of relatively simple agents, called ants, eciently carries out complex tasks such as resource optimization and control. Ants act according to a sequential decision making scheme in which a stochastic decision policy is adaptively built and evaluated by the ants themselves. As Dorigo, Bonabeau, and Theraulaz point out in the rst paper of this special issue, the indirect stigmergic communication among the ants is the key characteristic of ant algo
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Marco Dorigo, Gianni A. Di, Thomas Stützle · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · 2000
info:eu-repo/semantics/published
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2015
Abstract: Ant Colony Optimization (ACO) a nature-inspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. ACO algorithms have been modified in recent years to improve the performance of the first algorithm, posed by Dorigo. In this paper we compare different ACO algorithms and combine them in order to collect their advantages in an extended ACO algorithm.
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N. F. Britton, T. R. Stickland, N. R. Franks · Journal of Biological Systems · 1998
We consider some models of ant foraging and recruitment behaviour that depend on each individual ant following a simple algorithm. Self-organisation enables the colony as a whole to establish a foraging strategy in the absence of any hierarchical control. In this paper we investigate how the effectiveness of such a foraging strategy depends on the persistence of the signals used by the individual ants and on the errors they make in following such signals. The use of inhibitory as well as excitatory signals is considered, and shown to be extremely effective in certain circumstances. This is interesting, as such signals have never been observed in real ant colonies. Such models are often investigated by simulation, but we approach them from the point of view of statistical mechanics. Looked at another way, which yields some insight, we approximate the stochastic process that models the system by a diffusion process with small diffusion parameter. This approach does not replace simulation, but supplements it. Its advantage is that it can elucidate the role of parameters more clearly and using much less computer time than simulation, but its disadvantage is that many simplifying assumptions must be made before the problem is amenable to analytic treatment.
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