Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

New nature-inspired metaheuristics applied to the constrained optimization of a heavy-duty gas turbine operation

Orientador: Prof. Dr. Leandro dos Santos CoelhoTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 26/11/2020Inclui referências: p. 94-107Área de concentração: Sistemas EletrônicosResumo: Os codigos computacionais complexos das mais diversas areas, tais como industria 4.0 e energia, apresentam caracteristicas como nao-linearidade, escala, multimodalidade e presenca de restricoes. Por este motivo, as tecnicas classicas Newtonianas e baseadas em gradiente nao sao recomendadas para problemas de otimizacao global, os quais contem inumeras variaveis de projeto, restricoes e simulacoes incorporadas. Isso incentivou novas pesquisas em metaheuristicas baseadas em fenomenos naturais, principalmente comportamentos de animais com caracteristicas cooperativas ou colaborativas. Entretanto, nao existe um algoritmo unico capaz de ter bom desempenho para todos os tipos de problemas de otimizacao, o que justifica a busca recorrente por novas abordagens para solucionar esses problemas. Portanto, a presente tese introduz duas metaheuristicas com estruturas inovadoras inspiradas na natureza e nunca propostas. A primeira e baseada na especie Canis latrans e denominada Algoritmo de Otimizacao dos Coiotes (do ingles Coyote Optimization Algorithm, COA). A segunda, por sua vez, e inspirada na especie Cebus capucinus e denominada Otimizador dos Macacos-prego-da-cara-branca (do ingles Whitefaced Capuchin Monkeys Optimizer, WfCMO). Os algoritmos propostos sao avaliados sob um conjunto de funcoes de benchmarks empregadas nas competicoes do Congresso de Computacao Evolutiva (do ingles Congress on Evolutionary Computation, CEC) organizado pelo Instituto de Engenheiros Eletricistas e Eletronicos (do ingles Institute of Electrical and Electronics Engineers, IEEE) e comparadas a outras metaheuristicas inspiradas na natureza. Alem disso, a modelagem de um problema de otimizacao com restricoes de uma turbina a gas do tipo heavy-duty de uma termeletrica brasileira tambem e proposto nesta pesquisa. Para soluciona-lo, uma versao cultural do COA e proposta e seu desempenho e avaliado e comparado com outros algoritmos do estado-da-arte. Os resultados mostram que as metaheuristicas propostos nesta pesquisa alcancaram desempenho satisfatorio e superaram os outros algoritmos com 95% de confianca estatistica com base no teste nao-parametrico deWilcoxon-Mann-Whitney e tambem nos criterios do IEEE CEC 2017. Ainda, os resultados conquistados para problems multimodais e de alta dimensao mostram que as tecnicas sao promissoras para estes tipos de problema, que sao usuais em problemas reais. Ademais, as analises de curva de convergencia e de diversidade da populacao indicam um balanco adequado entre exploracao e aproveitamento. Por fim, a versao cultural do COA, que se demonstrou capaz de evitar convergencia prematura, superou os demais algoritmos do estado-da-arte para o problema de otimizacao da operacao da turbina. Palavras-chave: Industria 4.0, Inteligencia Computacional, Otimizacao Global, Metaheuristicas inspiradas na natureza.Abstract: The real-world applications from the most diverse fields such as industry 4.0 and energy have been formulated into complex computational codes with features as non-linearity, scale, multimodality, and the presence of constraints. Because of that, the classic Newtonians and gradient-based techniques are not recommended for global optimization applications with many design variables, constraints, and simulations embedded. It has encouraged new researches on metaheuristics based on natural phenomena, mainly animal behaviors with cooperative or collaborative features. However, there is not a unique algorithm able to perform well for all types of optimization problems, which justifies the recurrent search for new approaches. Hence, this thesis presents two never-proposed nature-inspired metaheuristics with innovative structures. The first one is based on the Canis latrans species and it is denoted Coyote Optimization Algorithm (COA). The second one is inspired by the Cebus capucinus species and receives the name of White-faced Capuchin Monkeys Optimizer (WfCMO). The proposed algorithms are evaluated under a set of benchmark functions employed in the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) competitions and compared to other state-of-the-art nature-inspired metaheuristics. Besides, the design of a constrained optimization problem of a heavy-duty gas turbine operation from a Brazilian thermoelectric power plant is proposed in this research. To solve it, a cultural version of the COA is proposed and its performance is evaluated and compared to other state-of-the-art algorithms. The results show that the proposed metaheuristics achieve profitable performance and outperform some state-of-the-art algorithms with 95% of statistical confidence based on the Wilcoxon-Mann- Whitney non-parametric test and the criteria of the IEEE CEC of 2017. Also, these algorithms present promising results for multimodal and high dimensional problems, which are the most usual features of real-world problems. Moreover, the convergence and diversity curves indicate a suitable balance between exploration and exploitation. Further, the proposed cultural version of the COA outperforms other state-of-the-art algorithms for the gas turbine operation problem. Its ability to avoid premature convergence is also demonstrated. Keywords: Industry 4.0, Computational Intelligence, Global Optimization, Nature-Inspired Metaheuristics

Tools for identifying biodiversity: progress and problems

The correct identification of organisms is fundamental not only for the assessment and the conservation of biodiversity, but also in agriculture, forestry, the food and pharmaceutical industries, forensic biology, and in the broad field of formal and informal education at all levels. In this book, the reader will find short presentations of current and upcoming projects (EDIT, KeyToNature, STERNA, Species 2000, Fishbase, BHL, ViBRANT, etc.), plus a large panel of short articles on software, taxonomic applications, use of e-keys in the educational field, and practical applications. Single-access keys are now available on most recent electronic devices; the collaborative and semantic web opens new ways to develop and to share applications; the automatic processing of molecular data and images is now based on validated systems; identification tools appear as an efficient support for environmental education and training; the monitoring of invasive and protected species and the study of climate change require intensive identifications of specimens, which opens new markets for identification research

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.