Application of ontologies in peer-to-peer database with routing queries based on ant colony algorithm

A significant set of information stored in different databases around the world, can be shared through peer-topeer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-topeer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. This paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. The main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results. © 2011 IEEE.

Architecture for peer-to-peer databases with routing queries using ant colony algorithm and semantic support

In a peer-to-peer network, the nodes interact with each other by sharing resources, services and information. Many applications have been developed using such networks, being a class of such applications are peer-to-peer databases. The peer-to-peer databases systems allow the sharing of unstructured data, being able to integrate data from several sources, without the need of large investments, because they are used existing repositories. However, the high flexibility and dynamicity of networks the network, as well as the absence of a centralized management of information, becomes complex the process of locating information among various participants in the network. In this context, this paper presents original contributions by a proposed architecture for a routing system that uses the Ant Colony algorithm to optimize the search for desired information supported by ontologies to add semantics to shared data, enabling integration among heterogeneous databases and the while seeking to reduce the message traffic on the network without causing losses in the amount of responses, confirmed by the improve of 22.5% in this amount. © 2011 IEEE.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Social and temporal organization in an ant colony

Ant colonies are known for their complex and efficient social organization that com-pletely lacks hierarchical structure. However, due to methodological difficulties in follow¬ing all ants of a colony, it was until now impossible to investigate the social and temporal organization of colonies. We developed a tracking system that allows tracking the posi¬tions and orientations of several hundred individually labeled ants continuously, providing for the first time quantitative long term data on all individuals of a colony. These data permit reconstructing trajectories, activity patterns and social networks of all ants in a colony and enable us to investigate ant behavior quantitatively in previously unpreceded ways. By analyzing the spatial positions and social interactions of all ants in six colonies for 41 days we show that ant colonies are organized in groups of nurses, nest patrollers and foragers. Workers of each group were highly interconnected and occupied similar spa¬tial locations in the nest. Groups strongly segregated spatially, and were characterized by unique behavioral signatures. Nurses spent most of their time on the brood. Nest patrollers frequently visited the rubbish pile, and foragers frequently visited the forag¬ing arena. In addition nurses were on average younger than nest patrollers who were, in turn, younger than foragers. We further show that workers had a preferred behav¬ioral trajectory and moved from nursing to nest patrolling, and from nest patrolling to foraging. By analyzing the activity patterns of all ants we show that only a third of all workers in a colony exhibit circadian rhythms and that these rhythms shortened by on av¬erage 42 minutes in constant darkness, thereby demonstrating the presence of a functional endogenous clock. Most rhythmic workers were foragers suggesting that rhythmicity is tightly associated with task. Nurses and nest patrollers were arrhythmic which most likely reflects plasticity of the circadian clock, as isolated workers in many species exhibit circadian rhythmicity. Altogether our results emphasize that ant colonies, despite their chaotic appearance, repose on a strong underlying social and temporal organization. - Les colonies de fourmis sont connues pour leur organisation sociale complexe et effi-cace, charactérisée par un manque absolu de structure hiérarchique. Cependant, puisqu'il est techniquement très difficile de suivre toutes les fourmis d'une colonie, il a été jusqu'à maintenant impossible d'étudier l'organisation sociale et temporelle des colonies de four-mis. Nous avons développé un système qui permet d'extraire en temps réel à partir d'images vidéo les positions et orientations de plusieurs centaines de fourmis marquées individuellement. Nous avons pu ainsi générer pour la première fois des données quanti-tatives et longitudinales relatives à des fourmis appartenant à une colonie. Ces données nous ont permis de reconstruire la trajectoire et l'activité de chaque fourmi ainsi que ses réseaux sociaux. Ceci nous a permis d'étudier de manière exhaustive et objective le com-portement de tous les individus d'une colonie. En analysant les données spatiales et les interactions sociales de toutes les fourmis de six colonies qui ont été filmées pendant 41 jours, nous montrons que les fourmis d'une même colonie se répartissent en trois groupes: nourrices, patrouilleuses et approvisionneuses. Les fourmis d'un même groupe interagis-sent fréquemment et occupent le même espace à l'intérieur du nid. L'espace propre à un groupe se recoupe très peu avec celui des autres. Chaque groupe est caractérisé par un comportement typique. Les nourrices s'affairent surtout autour du couvain. Les pa-trouilleuses font de fréquents déplacements vers le tas d'ordures, et les approvisionneuses sortent souvent du nid. Les nourrices sont en moyenne plus jeunes que les patrouilleuses qui, à leur tour, sont plus jeunes que les approvisionneuses. De plus, nous montrons que les ouvrières changent de tâche au cours de leur vie, passant de nourrice à patrouilleuse puis à approvisionneuse. En analysant l'activité de chaque fourmi, nous montrons que seulement un tiers des ouvrières d'une colonie présente des rythmes circadiens et que ces rythmes diminuent en moyenne de 42 minutes lorsqu'il y a obscurité constante, ce qui démontre ainsi la présence d'une horloge endogène. De plus, la plupart des approvi¬sionneuses ont une activité rythmique alors que les nourrices et patrouilleuses présentent une activité arythmique, ce qui suggère que la rythmicité est étroitement associée à la tâche. L'arythmie des nourrices et patrouilleuses repose probablement sur une plasticité de l'horloge endogène car des ouvrières de nombreuses espèces font preuve d'une ryth¬micité circadienne lorsqu'elles sont isolées de la colonie. Dans l'ensemble nos résultats révèlent qu'une colonie de fourmis se fonde sur une solide organisation sociale et tem¬porelle malgré son apparence chaotique.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Ant colony for TSP

The aim of this work is to investigate Ant Colony Algorithm for the traveling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. This paper is based on the ideas of ant colony algorithm and analysis the main parameters of the ant colony algorithm. Experimental results for solving TSP problems with ant colony algorithm show great effectiveness.

A modified ant colony optimization algorithm modeled on tabu-search methods

An earlier model underlying the foraging strategy of a pachycodyla apicalis ant is modified. The proposed algorithm incorporates key features of the tabu-search method in the development of a relatively simple but robust global ant colony optimization algorithm. Numerical results are reported to validate and demonstrate the feasibility and effectiveness of the proposed algorithm in solving electromagnetic (EM) design problems.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Algoritmos de alinhamento múltiplo e técnicas de otimização para esses algoritmos utilizando Ant Colony

Pós-graduação em Ciência da Computação - IBILCE

Sequenzierung mit Ant-Colony-Systemen am Beispiel Querverteil-Wagen

Dieser Beitrag zeigt die Anwendung des Ant-Colony-System (ACS) Algorithmus auf die Sequenzierung von Querverteil-Wagen in einem Lager. Wir erweitern den Basisalgorithmus der Ant-Colony-Optimierung (ACO) für die Minimierung der Bearbeitungszeit einer Menge von Fahraufträgen für die Querverteil-Wagen. Im Vergleich zu dem Greedy-Algorithmus ist der ACO-Algorithmus wettbewerbsfähig und schnell. In vielen Lagerverwaltungssystemen werden die Fahraufträge nach dem FIFO-Prinzip (First-in-First-out) ausgeführt. In diesem Beitrag wird der ACO-Algorithmus genutzt, um eine optimale Sequenz der Fahraufträge zu bilden.

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.