Organization model for mobile wireless sensor networks inspired in artificial bee colony

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise do Desempenho de Técnicas de Otimização no Problema de Escalonamento

A optimização nas aplicações modernas assume um carácter fortemente interdisciplinar, relacionando-se com a necessidade de integração de diferentes técnicas e paradigmas na resolução de problemas reais complexos. O problema do escalonamento é recorrente no planeamento da produção. Sempre que uma ordem de fabrico é lançada, é necessário determinar que recursos serão utilizados e em que sequência as atividades serão executadas, para otimizar uma dada medida de desempenho. Embora ainda existam empresas a abordar o problema do escalonamento através de simples heurísticas, a proposta de sistemas de escalonamento tem-se evidenciado na literatura. Pretende-se nesta dissertação, a realização da análise de desempenho de Técnicas de Optimização, nomeadamente as meta-heurísticas, na resolução de problemas de optimização complexos – escalonamento de tarefas, particularmente no problema de minimização dos atrasos ponderados, 1||ΣwjTj. Assim sendo, foi desenvolvido um protótipo que serviu de suporte ao estudo computacional, com vista à avaliação do desempenho do Simulated Annealing (SA) e o Discrete Artificial Bee Colony (DABC). A resolução eficiente de um problema requer, em geral, a aplicação de diferentes métodos, e a afinação dos respetivos parâmetros. A afinação dos parâmetros pode permitir uma maior flexibilidade e robustez mas requer uma inicialização cuidadosa. Os parâmetros podem ter uma grande influência na eficiência e eficácia da pesquisa. A sua definição deve resultar de um cuidadoso esforço experimental no sentido da respectiva especificação. Foi usado, no âmbito deste trabalho de mestrado, para suportar a fase de parametrização das meta-heurísticas em análise, o planeamento de experiências de Taguchi. Da análise dos resultados, foi possível concluir que existem vantagem estatisticamente significativa no desempenho do DABC, mas quando analisada a eficiência é possível concluir que há vantagem do SA, que necessita de menos tempo computacional.

Estudi comparatiu de l'algorisme basat en una colònia artificial d'abelles

La finalitat d'aquest projecte és la realització d'un estudi comparatiu de l'algoritme basat en una colònia artificial d'abelles, Artificial Bee Colony (ABC), comparat amb un conjunt d'algoritmes fonamentats en el paradigma de la computació evolutiva. S'utilitzarà l'eficàcia a l'hora d'optimitzar diverses funcions com a mesura comparativa. Els algoritmes amb els quals es comparara l'algoritme ABC són: algoritmes genètics, evolució diferencial i optimització amb eixam de partícules.

Clinical signs of deformed wing virus infection are predictive markers for honey bee colony losses.

The ectoparasitic mite Varroa destructor acting as a virus vector constitutes a central mechanism for losses of managed honey bee, Apis mellifera, colonies. This creates demand for an easy, accurate and cheap diagnostic tool to estimate the impact of viruliferous mites in the field. Here we evaluated whether the clinical signs of the ubiquitous and mite-transmitted deformed wing virus (DWV) can be predictive markers of winter losses. In fall and winter 2007/2008, A.m. carnica workers with apparent wing deformities were counted daily in traps installed on 29 queenright colonies. The data show that colonies which later died had a significantly higher proportion of workers with wing deformities than did those which survived. There was a significant positive correlation between V. destructor infestation levels and the number of workers displaying DWV clinical signs, further supporting the mite's impact on virus infections at the colony level. A logistic regression model suggests that colony size, the number of workers with wing deformities and V. destructor infestation levels constitute predictive markers for winter colony losses in this order of importance and ease of evaluation.

Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Prediction of new crystal structures under extreme conditions

One of the most important challenges in chemistry and material science is the connection between the contents of a compound and its chemical and physical properties. In solids, these are greatly influenced by the crystal structure.rnrnThe prediction of hitherto unknown crystal structures with regard to external conditions like pressure and temperature is therefore one of the most important goals to achieve in theoretical chemistry. The stable structure of a compound is the global minimum of the potential energy surface, which is the high dimensional representation of the enthalpy of the investigated system with respect to its structural parameters. The fact that the complexity of the problem grows exponentially with the system size is the reason why it can only be solved via heuristic strategies.rnrnImprovements to the artificial bee colony method, where the local exploration of the potential energy surface is done by a high number of independent walkers, are developed and implemented. This results in an improved communication scheme between these walkers. This directs the search towards the most promising areas of the potential energy surface.rnrnThe minima hopping method uses short molecular dynamics simulations at elevated temperatures to direct the structure search from one local minimum of the potential energy surface to the next. A modification, where the local information around each minimum is extracted and used in an optimization of the search direction, is developed and implemented. Our method uses this local information to increase the probability of finding new, lower local minima. This leads to an enhanced performance in the global optimization algorithm.rnrnHydrogen is a highly relevant system, due to the possibility of finding a metallic phase and even superconductor with a high critical temperature. An application of a structure prediction method on SiH12 finds stable crystal structures in this material. Additionally, it becomes metallic at relatively low pressures.

A novel hybrid swarm based approach for curriculum based course timetabling problem

This work applies a hybrid approach in solving the university curriculum-based course timetabling problem as presented as part of the 2nd International Timetabling Competition 2007 (ITC2007). The core of the hybrid approach is based on an artificial bee colony algorithm. Past methods have applied artificial bee colony algorithms to university timetabling problems with high degrees of success. Nevertheless, there exist inefficiencies in the associated search abilities in term of exploration and exploitation. To improve the search abilities, this work introduces a hybrid approach entitled nelder-mead great deluge artificial bee colony algorithm (NMGD-ABC) where it combined additional positive elements of particle swarm optimization and great deluge algorithm. In addition, nelder-mead local search is incorporated into the great deluge algorithm to further enhance the performance of the resulting method. The proposed method is tested on curriculum-based course timetabling as presented in the ITC2007. Experimental results reveal that the proposed method is capable of producing competitive results as compared with the other approaches described in literature

Prognóstico de falhas baseado em redes neurais com estados de eco

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2015.

BEE COLONIES APPLIED TO MULTIPROCESSOR SCHEDULING

In order to achieve the high performance, we need to have an efficient scheduling of a parallelprogram onto the processors in multiprocessor systems that minimizes the entire executiontime. This problem of multiprocessor scheduling can be stated as finding a schedule for ageneral task graph to be executed on a multiprocessor system so that the schedule length can be minimize [10]. This scheduling problem is known to be NP- Hard.In multi processor task scheduling, we have a number of CPU’s on which a number of tasksare to be scheduled that the program’s execution time is minimized. According to [10], thetasks scheduling problem is a key factor for a parallel multiprocessor system to gain betterperformance. A task can be partitioned into a group of subtasks and represented as a DAG(Directed Acyclic Graph), so the problem can be stated as finding a schedule for a DAG to beexecuted in a parallel multiprocessor system so that the schedule can be minimized. Thishelps to reduce processing time and increase processor utilization. The aim of this thesis workis to check and compare the results obtained by Bee Colony algorithm with already generatedbest known results in multi processor task scheduling domain.

Evaluation of Cage Designs and Feeding Regimes for Honey Bee (Hymenoptera: Apidae) Laboratory Experiments

The aim of this study was to improve cage systems for maintaining adult honey bee (Apis mellifera L.) workers under in vitro laboratory conditions. To achieve this goal, we experimentally evaluated the impact of different cages, developed by scientists of the international research network COLOSS (Prevention of honey bee COlony LOSSes), on the physiology and survival of honey bees. We identified three cages that promoted good survival of honey bees. The bees from cages that exhibited greater survival had relatively lower titers of deformed wing virus, suggesting that deformed wing virus is a significant marker reflecting stress level and health status of the host. We also determined that a leak- and drip-proof feeder was an integral part of a cage system and a feeder modified from a 20-ml plastic syringe displayed the best result in providing steady food supply to bees. Finally, we also demonstrated that the addition of protein to the bees' diet could significantly increase the level ofvitellogenin gene expression and improve bees' survival. This international collaborative study represents a critical step toward improvement of cage designs and feeding regimes for honey bee laboratory experiments.

Neonicotinoid insecticides severely affect honey bee queens

Queen health is crucial to colony survival of social bees. Recently, queen failure has been proposed to be a major driver of managed honey bee colony losses, yet few data exist concerning effects of environmental stressors on queens. Here we demonstrate for the first time that exposure to field realistic concentrations of neonicotinoid pesticides during development can severely affect queens of western honey bees (Apis mellifera). In pesticide-exposed queens, reproductive anatomy (ovaries) and physiology (spermathecal-stored sperm quality and quantity), rather than flight behaviour, were compromised and likely corresponded to reduced queen success (alive and producing worker offspring). This study highlights the detriments of neonicotinoids to queens of environmentally and economically important social bees, and further strengthens the need for stringent risk assessments to safeguard biodiversity and ecosystem services that are vulnerable to these substances.

The number of reproductive workers in highly eusocial Hymenoptera: monogyny and monandry

Haplodiploidy results in relatedness asymmetries between colony members of highly eusocial Hymenoptera. As a consequence, queen and reproductive workers are more related to their own sons than to each other's male offspring. Kin selection theory predicts multiple optima in male parentage: either the queen or the workers should produce all the males. Nevertheless, shared male parentage is common in highly eusocial hymenopterans. An inclusive fitness model was used to analyze the effect of the number of reproductive workers on male parentage shared by the queen and laying workers by isolating the male component from an inclusive fitness equation using the equal fitness through male condition for each pairwise combination of the three female classes comprised of the queen, laying workers and non-laying workers. The main result of the theoretical analyses showed that the fraction of males produced by workers increases asymptotically with the number of laying workers at an increasingly diminishing rate, tending to an asymptotic value of 0.67. In addition, as the number of laying workers increases, the share of male parentage converges to that of non-laying workers. The diminishing return effect on male parentage share depending on the number of reproductive workers leads us to expect the number of reproductive workers to be relatively small in a stingless bee colony, even in the absence of productivity costs. The available data confirms this hypothesis, as there is an unusually small number of reproductive workers in stingless bee colonies.

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.