Image Segmentation Using Biogeography Based Optimization (BBO)

Image segmentation is an important problem in computer vision to completely understand the image for better results, i.e., identification of homogeneous regions in the image and has been the subject of considerable research for over the last three decades. Many algorithms have been elaborated for this purpose. This paper elaborates two algorithms one is global optimization method Biogeography Based optimization for automatically grouping the pixels of an color image into disjoint homogeneous regions and the other is clustering method Fuzzy K-means algorithm for reducing the computational complexity of image. And then comparison between both the techniques is calculated. In this purposed work these two algorithms are applied to image and performance is evaluated on the basis of computational time. Fuzzy K-means produces results which require more computational time than Biogeography based optimization. Therefore, comparison shows that Biogeography Based Optimization is more reliable and faster approach for image segmentation than Fuzzy K-means clustering algorithm

Mixed Pixel Resolution by Evolutionary Algorithm: A Survey

Now a day2019;s Remote Sensing is a mature research area. Remote sensing is defined as a technique for acquiring the information about an object without making physical contact with that image via remote sensors. But the major problem of remotely sensed images is mixed pixel which always degrades the image quality. Mixed pixels are usually the biggest reason for degrading the success in image classification and object recognition. Another major problem is the decomposition of mixed pixels precisely and effectively. Remote sensing data is widely used for the classification of types of features such as vegetation, water body etc but the problem occurs in tagging appropriate class to mixed pixels. In this paper we attempted to present an approach for resolving the mixed pixels by using optimization algorithm i.e. Biogeography based optimization. The main idea is to tag the mixed pixel to a particular class by finding the best suitable class for it using the BBO parameters i.e. Migration and Mutation

Analysis of Migration Models of Biogeography-based Optimization Using Markov Theory

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography theory and previous results, in this paper a generalized sinusoidal migration model curve is proposed. A previously derived BBO Markov model is used to analyze the effect of migration models on optimization performance, and new theoretical results which are confirmed with simulation results are obtained. The results show that the generalized sinusoidal migration model is significantly better than other models for simple but representative problems, including a unimodal one-max problem, a multimodal problem, and a deceptive problem. In addition, performance comparison is further investigated through 23 benchmark functions with a wide range of dimensions and diverse complexities, to verify the superiority of the generalized sinusoidal migration model

Markov Models for Biogeography-based Optimization

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the science and study of the geographical distribution of biological organisms. In BBO, problem solutions are analogous to islands, and the sharing of features between solutions is analogous to the migration of species. This paper derives Markov models for BBO with selection, migration, and mutation operators. Our models give the theoretically exact limiting probabilities for each possible population distribution for a given problem. We provide simulation results to confirm the Markov models

Blended Biogeography-based Optimization for Constrained Optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems

Variations of Biogeography-based Optimization and Markov Analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigration-based BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigration-based BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems

Hierarchical models for the anlaysis of species distributions and abundances: development and applications

There is a strong need for statistical methods that can maximize the utility of ecological data while providing accurate estimates of species abundances and distributions. This dissertation aims to build on current statistical models using Bayesian hierarchical approaches to advance these methods. Chapters one, two, and three utilize a multi-species modeling framework to estimate species occurrence probabilities. Chapter one presents a model to assess the community response of breeding birds to habitat fragmentation. The results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness. Chapter two demonstrates how the multi-species modeling framework can be used to evaluate conservation actions through a component that incorporates species-specific responses to management treatments. In Chapter three, I develop a method for validating predictions generated by the multi-species model that accounts for detection biases in evaluation data. I build competing models using wetland breeding amphibian data and test their abilities to predict occupancy at unsampled locations. Chapters four and five develop count models that are used to estimate population abundances in relation to environmental and climate variables. In Chapter four, I employ a Poisson regression designed to determine how climate affects the annual abundances of migrating monarch butterflies. I incorporate the climate conditions experienced both during a spring migration phase, as well as during summer recruitment. In Chapter five, I analyze sea duck data to characterize the spatial and temporal distributions along the U.S. and Canadian Atlantic coast. I model count data for five species using a zero-inflated negative binomial model that includes latitude, habitat covariates, and the North Atlantic Oscillation. The results from these two chapters demonstrate how Bayesian models can be used to elucidate complicated species-climate relationships. The chapters of this dissertation illustrate creative development and application of advanced statistical methods to complex biological systems. These applications provide a practical framework for dealing with highly aggregated species and uneven species distributions in community analyses, as well as a method for evaluating occurrence estimates that accounts for detection biases. My results highlight the dynamic relationships between population and community structure, habitat, and climate

Speciation Associated with Shifts in Migratory Behavior in an Avian Radiation

Gómez-Bahamón et al. show that speciation is associated with changes in migratory behavior in fork-tailed flycatchers (Tyrannus savana). Divergence occurred through loss of migratory behavior of a single lineage. This mode of speciation likely occurred across New World flycatchers (Tyrannidae).Fil: Gómez Bahamón, Valentina. Field Museum of Natural History; Estados Unidos. Investigación Para la Conservación En El Neotrópico; Colombia. University of Illinois; Estados Unidos. Universidad de los Andes; ColombiaFil: Márquez, Roberto. University of Chicago; Estados UnidosFil: Jahn, Alex. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Miyaki, Cristina Yumi. Universidade de Sao Paulo; BrasilFil: Tuero, Diego Tomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Laverde, Oscar. Universidad de los Andes; Colombia. Pontificia Universidad Javeriana; ColombiaFil: Restrepo, Silvia. Universidad de los Andes; ColombiaFil: Cadena, Carlos Daniel. Universidad de los Andes; Colombi

Functional traits and resource-use strategies of native and invasive plants in Eastern North American forests

Despite the presumption that native species are well adapted to their local environment, non-native invaders seem to outperform native plants. Intuitively, it appears paradoxical that non-native species, with no opportunity for local adaptation, can exhibit greater fitness than native plants with this advantage. Here, I compared traits of native and invasive shrub and liana species in Eastern North American (ENA) forests to test the overarching hypothesis that non-native understory species invasive to this region have superior resource-use strategies, or alternatively, they share the same metabolic tradeoffs as the native flora. First, at a global scale, I addressed the largely untested hypothesis that biogeography places significant constraints on trait evolution. Reanalyzing a large functional trait database, along with species\u27 native distribution data, I found that regional floras with different evolutionary histories exhibit different tradeoffs in resource capture strategies. Second, using a common garden to control for environment, I measured leaf physiological traits relating to resource investments, carbon returns, and resource-use efficiencies in 14 native and 18 non-native invasive species of common genera found in ENA understories, where growth is presumably constrained by light and nutrient limitation. I tested whether native and invasive plants have similar metabolic constraints or if these invasive species (predominantly from East Asia) are more productive per unit resource cost. Despite greater resource costs (leaf construction, leaf N), invaders exhibited greater energy- and nitrogen-use efficiencies, particularly when integrated over leaf lifespan. Efficiency differences were primarily driven by greater mean photosynthetic abilities (20% higher daily C gain) and leaf lifespans (24 days longer) in invasive species. Third, motivated by common garden results, I conducted a resource addition experiment in a central NY deciduous forest to investigate the role of resource limitation on invasion success in the field. I manipulated understory light environments (overstory tree removal) and N availabilities (ammonium-nitrate fertilization) to create a resource gradient across plots each containing 3 invasive and 6 native woody species. Invasive species generally exhibited greater aboveground productivity and photosynthetic gains. After two treatment years, invasive species displayed more pronounced trait responses to the resource gradients, primarily light, relative to the weaker responses of native species. Lastly, I asked whether species exhibit similar resource-use strategies in their native and invasive ranges. I measured leaf functional traits of Rhamnus cathartica (native to Europe, invasive in ENA) and Prunus serotina (native to ENA, invasive in Europe) in populations across central NY and northern France. Notably, I found invasive US populations of R. cathartica had markedly greater photosynthetic rates (50% higher) and reduced leaf N resorption rates in autumn (30% lower) than native French populations. Contrastingly, I found minimal leaf trait differences in P. serotina between native (US) and invasive (French) populations. Collectively, my results highlight the utility of functional trait perspectives and support a mechanistic explanation for invasion success based on differential abilities of species to convert limiting resources to biomass