The aim of the work is an analysis influence of years and altitude above sea levels on the honey production of sample localitys in different altitude above sea levels. For observation of the years 2002{--}2006 was choice five localitys in different altitude above sea levels (472 {--} 650 m). The interaction sea level and year is a determine factor wich influence honey production of one bee colony from 89.77%. On increase sea level about 100 m is speeding of the honey production about 5 kg of one bee colony. Than the direct influence one factor (altitude above sea level, year) is strikinger on the honey production their interaction.

Artificial Bee Colony Algorithm är en algoritm som tidigare tillämpats på många numeriska optimeringsproblem. Algoritmen är dock inte vanligt förekommande vad gäller vägplanering i dataspel. Detta arbete undersöker ifall algoritmen presterar bättre än A* på fyra olika testmiljöer eftersom A* är en av de mest använda algoritmerna för vägplanering i dataspel och således en bra referens. De aspekter som jämförs vid mätningarna är algoritmernas tidsåtgång samt längden på de resulterande vägarna. En riktad slumpgenerering av vägar har implementerats till algoritmen som gör att den inte fungerar på djupa återvändsgränder. Algoritmen har också en roulettehjulsselektion samt egenskapen att kunna generera slumpade grannvägar till de som skapats hittills. Resultaten visar att Artificial Bee Colony Algorithm presterar betydligt sämre än A* och att algoritmen därför inte är en bättre algoritm för vägplanering i dataspel. Algoritmen har dock potential till att prestera bättre och fungera på återvändsgränder om man förbättrar dess slumpgenerering av vägar.

Finite Element Model Updating has recently arisen as an issue of vast importance on the design, construction and maintenance of structures in civil engineering. Many algorithms have been proposed, developed and enhanced in order to accomplish the demands of the updating process, mainly to achieve computationally efficient programs and greater results.The present Master Thesis proposes two new algorithms to be used in Finite Element Model Updating: the Bees Algorithms (BA) and the Artificial Bee Colony algorithm (ABC). Both were first proposed in 2005, are based on the foraging behaviour of bees and have been proved to be efficient algorithms in other fields. The objective of this Master Thesis is, thus, to implement and to test these two newalgorithms in Finite Element Model Updating for a cantilever beam. The Finite Element Model and the algorithms are programmed, followed by the extraction of the experimental frequencies and the updating process. Results, comparison of these two methods and conclusions are given at the end of this report, as well as suggestions for further work.

yes / This paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08’s equations exhibit a reasonable performance but Persson and Felekoglu et al.’s equations are highly inaccurate for the prediction of SCC elastic modulus.

Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, skirstymo uždaviniai ir jų formuluotės, bei praktinės interpretacijos, plačiau apžvelgiami ir analizuojami dirbtinių bičių kolonijų algoritmai. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą skirstymo uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę. / This paper consists of short descriptions of swarm systems algorithms, assigment problems and longer overview of artificial bee colony algorithms and it‘s analysis. Moreover, you can find an Artificial Bee Colony Algorithm's Application to one of an Assigment Problems and it's computational results analysis.

Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, maršrutų optimizavimo uždaviniai ir jų formuluotės, bei praktinės interpretacijos. Plačiau apžvelgiami dirbtinių bičių kolonijų algoritmai ir jų pritaikymas keliaujančio pirklio uždaviniams spręsti. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą keliaujančio pirklio uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę. / This paper consists of short description of swarm systems algorithms, route optimisation problems overview and longer description of artificial bee colony algorithms adaptation for traveling salesman problem. Moreover, you can find an artificial bee colony algorithm's application to traveling salesman problem and analysis of computational results.

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas. / Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas. / Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas. / Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.

This master thesis is exploring Beekeepers usage of Internet of Things, or “Internet of Bees”. Since most of the prior contributions are focusing on data gathering, the approach to focus on the users needs is central to take next steps in the field of using IoT for Beekeeping. After the introduction a chapter with an overview of current research and commercial solutions are presented. This is followed by a quantitative study with 222 responds, answering what beekeepers like to know about their bees, what platforms used by end users and what the beekeeper as a user expects. An demo of an existing commercial system is set up in real conditions, describing how to mount and configure a demo. Communication, synchronization and presentation is described. A closed user interface and a public user interface are a part of the demonstration. Potential users of this technique are interviewed to gain better understanding of users opinion of the demo. This is followed by another demo using a free of charge app where sound analysis processed with AI is tested. This thesis explains what beekeepers as users of Internet of Things could gain added value to their beekeeping.