Task partitioning in insect societies. I. Effect of colony size on queueing delay and colony ergonomic efficiency

The collection and handling of colony resources such as food, water, and nest construction material is often divided into subtasks in which the material is passed from one worker to another. This is known as task partitioning. When material is transferred directly from one individual to another, queueing delays frequently occur because individuals must sometimes wait for a transfer partner. A stochastic simulation model was written to study the effect of colony size on these delays. Queueing delay decreases roughly exponentially with colony size because stochastic fluctuations in the arrival of individuals are lower in larger colonies. These results support empirical studies of Polybia occidentalis and other theoretical studies of honeybees. The effect of the relative number of individuals in the two subtask groups was also studied. There is a unique optimal ratio of the number of workers associated with each of the subtasks that simultaneously minimizes mean queueing delay and maximizes colony nectar-processing rate. Deviations from this optimal ratio, for example, as a result of forager mortality or changes in nectar productivity that affect foraging trip duration, increase mean queueing delays greatly, especially in smaller colonies

A Study Of Vantage Point Neighbourhood Search In The Bees Algorithm For Combinatorial Optimization Problems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc. ) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tez çalışmasının temel amacı arıların kaynak arama davranışlarını modelleyen arı algoritmasının, kombinatoryal uzaylarda komşuluk arama fazına yeni bir yaklaşım geliştirilmesidir. Geliştirilen yaklaşım Gezgin Satıcı Problemine uygulanarak Gezgin Satıcı Problemi çözümünün en iyilenmesi amaçlanmıştır.This thesis focuses on nature-inspired optimisation algorithms, in particular, the Bees Algorithm that developed for combinatorial domains with new local search procedure and applied to Traveller Salesman Problem (TSP). An efficient and robust local neighborhood search algorithm is proposed for combinatorial domains to increase the efficiency of the Bees Algorithm.Yüksek LisansM.Sc

Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

The optimization algorithms that imitate nature have acquired much attention principally mechanisms for solving the difficult issues for example the travelling salesman problem (TSP) which is containing routing and scheduling of the tasks. This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. Bees Algorithm is one of the optimization algorithms inspired from the natural foraging ways of the honey bees of finding the best solution. It is a series of activities based on the searching algorithm in order to access the best solutions. It is an iteration algorithm; therefore, it is suffering from slow convergence. The other downside of the Bee Algorithm is that it has needless computation. This means that it spends a long time for the bees algorithm converge the optimum solution. In this study, the parallel bees algorithm technique is proposed for overcoming of this issue. Due to that, this would lead to reduce the required time to get a solution with faster results accuracy than original Bees Algorithm

The evolution of Cayaponia (Cucurbitaceae)

Premise of the study: The Cucurbitaceae genus Cayaponia comprises ∼60 species that occur from Uruguay to the southern United States and the Caribbean; C. africana occurs in West Africa and on Madagascar. Pollination is by bees or bats, raising the question of the evolutionary direction and frequency of pollinator shifts. Studies that investigated such shifts in other clades have suggested that bat pollination might be an evolutionary end point. Methods: Plastid and nuclear DNA sequences were obtained for 50 accessions representing 30 species of Cayaponia and close relatives, and analyses were carried out to test monophyly, infer divergence times, and reconstruct ancestral states for habitat preferences and pollination modes. Key results: The phylogeny shows that Cayaponia is monophyletic as long as Selysia (a genus with four species from Central and South America) is included. The required nomenclatural transfers are made in this paper. African and Madagascan accessions of C. africana form a clade that is part of a polytomy with Caribbean and South American species, and the inferred divergence time of 2–5 Ma implies a transoceanic dispersal event from the New World to Africa. The ancestral state reconstructions suggest that Cayaponia originated in tropical forests from where open savannas were reached several times and that bee pollination arose from bat pollination, roughly concomitant with the shifts from forests to savanna habitats. Conclusions: Cayaponia provides the first example of evolutionary transitions from bat to bee pollination as well as another instance of transoceanic dispersal from the New World to Africa

Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms

Polymeric materials, being capable of high mouldability, usability of long lifetime up to 50 years and availability at low cost properties compared to metallic materials, are in demand but finite element-based design engineers have limited means in terms of the limited material data and mathematical models. In particular, in the analysis of products with complex geometry, the stresses and strains of various amounts formed in the product should be known and evaluated in terms of a precise design of the product to fulfil life expectancy. Due to time and cost constraints, experimental data cannot be available for all cases required in analysis, therefore, finite element method-based simulations are commonly used by design engineers. This is also computationally expensive and requires a simpler and more precise way to complete the design more realistically. In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN models have provided much accurate and precise results, which are in line with 10% accuracy of experimental data