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Evolution of Wireless Communication Ecosystems (The ComSoc Guides to Communications Technologies)
by Suat SecginEvolution of Wireless Communication Ecosystems Understand a world transformed by wireless communication with this groundbreaking guide Since the advent of the internet, few technologies have proven more transformative than wireless communication. Never have we lived in a more comprehensively connected world, with the cloud and the coming sixth generation (6G) of wireless technology creating a vast and interconnected communications infrastructure. Global citizens of this newly interconnected reality are grappling like never before with its many challenges. Evolution of Wireless Communication Ecosystems provides readers with a history of wireless communication and a thorough overview of emerging frontiers. It traces wireless communication from the first generation through to the current fifth before surveying the current state of wireless technology and the ongoing research into 6G. The result is a book that understands wireless communication for the first time as an ecosystem, endlessly interconnected, growing, and boundlessly complex, but made intelligible by this highly readable introduction. Readers will also find: Detailed explanations of the journey starting from 1G to 6G Descriptions the infrastructure of 4G, 5G, and 6G systems, this all-connected communication ecosystem, the sub-components of this ecosystem, and the relationship among them Depictions of events seen in the capillaries of the communication echo system that show switching techniques, modulation, and multiplexing techniques Coverage of access techniques, protocols, the methods used in M2M and IoT connections at the endpoints, and security issues that show how they are an integral part of wireless communication infrastructure Evolution of Wireless Communication Ecosystems from 1G to 6G is an essential reference for wireless and telecommunications professionals, as well as researchers interested in 6G or other emerging wireless technologies.
Evolution "On Purpose": Teleonomy in Living Systems (Vienna Series in Theoretical Biology)
by Peter A. Corning, et al.A unique exploration of teleonomy—also known as &“evolved purposiveness&”—as a major influence in evolution by a broad range of specialists in biology and the philosophy of science.The evolved purposiveness of living systems, termed &“teleonomy&” by chronobiologist Colin Pittendrigh, has been both a major outcome and causal factor in the history of life on Earth. Many theorists have appreciated this over the years, going back to Lamarck and even Darwin in the nineteenth century. In the mid-twentieth century, however, the complex, dynamic process of evolution was simplified into the one-way, bottom-up, single gene-centered paradigm widely known as the modern synthesis. In Evolution &“On Purpose,&” edited by Peter A. Corning, Stuart A. Kauffman, Denis Noble, James A. Shapiro, Richard I. Vane-Wright, and Addy Pross, some twenty theorists attempt to modify this reductive approach by exploring in depth the different ways in which living systems have themselves shaped the course of evolution.Evolution &“On Purpose&” puts forward a more inclusive theoretical synthesis that goes far beyond the underlying principles and assumptions of the modern synthesis to accommodate work since the 1950s in molecular genetics, developmental biology, epigenetic inheritance, genomics, multilevel selection, niche construction, physiology, behavior, biosemiotics, chemical reaction theory, and other fields. In the view of the authors, active biological processes are responsible for the direction and the rate of evolution. Essays in this collection grapple with topics from the two-way &“read-write&” genome to cognition and decision-making in plants to the niche-construction activities of many organisms to the self-making evolution of humankind. As this collection compellingly shows, and as bacterial geneticist James Shapiro emphasizes, &“The capacity of living organisms to alter their own heredity is undeniable.&”
Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management
by Jorge Magalhães-Mendes David GreinerThis book focuses on civil and structural engineering and construction management applications. The contributions constitute modified, extended and improved versions of research presented at the minisymposium organized by the editors at the ECCOMAS conference on this topic in Barcelona 2014.
Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems (Computational Methods in Applied Sciences #49)
by Esther Andrés-Pérez Leo M. González Jacques Periaux Nicolas Gauger Domenico Quagliarella Kyriakos GiannakoglouThis book contains thirty-five selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2017). This was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Topics treated in the various chapters reflect the state of the art in theoretical and numerical methods and tools for optimization, and engineering design and societal applications. The volume focuses particularly on intelligent systems for multidisciplinary design optimization (mdo) problems based on multi-hybridized software, adjoint-based and one-shot methods, uncertainty quantification and optimization, multidisciplinary design optimization, applications of game theory to industrial optimization problems, applications in structural and civil engineering optimum design and surrogate models based optimization methods in aerodynamic design.
Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling (Adaptation, Learning, and Optimization #26)
by Kyle Robert Harrison Saber Elsayed Ivan Leonidovich Garanovich Terence Weir Sharon G. Boswell Ruhul Amin SarkerThis book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times.It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.
Evolutionary Artificial Intelligence: Proceedings of ICEAI 2023 (Algorithms for Intelligent Systems)
by David Asirvatham Francisco M. Gonzalez-Longatt Przemyslaw Falkowski-Gilski R. KanthavelThis book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2023) held in Malaysia during 13-14 September 2023. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.
Evolutionary Computation and Complex Networks
by Jing Liu Hussein A. Abbass Kay Chen TanThis book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.
Evolutionary Computation in Scheduling
by Amir H. Gandomi Ali Emrouznejad Mo M. Jamshidi Kalyanmoy Deb Iman RahimiPresents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches. Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0 (Demystifying Technologies for Computational Excellence)
by R. S. ChauhanThis book focuses on cutting-edge innovations and core theories, principles, and algorithms applicable to a wide area. Real-life applications, case studies, and examples are included along with emerging trends, design, and optimized solutions pivoting around the needs of Society 5.0. Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0 provides a holistic view of evolutionary computation techniques including principles, procedures, and future applications with real-life examples. The book comprehensively explains evolutionary computation, design, principles, development trends, and optimization and describes how it can transform the operating context of the organization. It exemplifies the potential of evolutionary computation for the next generation and the role of cloud computing in shaping Society 5.0. It also provides insight into various platforms, paradigms, techniques, and tools used in diverse fields. This book appeals to a variety of readers such as academicians, researchers, research scholars, and postgraduates.
Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (Lecture Notes on Data Engineering and Communications Technologies #53)
by V. Suma Noureddine Bouhmala Haoxiang WangThis book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20–21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks.
Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2021 (Lecture Notes on Data Engineering and Communications Technologies #116)
by V. Suma Xavier Fernando Ke-Lin Du Haoxiang WangThis book mainly reflects the recent research works in evolutionary computation technologies and mobile sustainable networks with a specific focus on computational intelligence and communication technologies that widely ranges from theoretical foundations to practical applications in enhancing the sustainability of mobile networks. Today, network sustainability has become a significant research domain in both academia and industries present across the globe. Also, the network sustainability paradigm has generated a solution for existing optimization challenges in mobile communication networks. Recently, the research advances in evolutionary computing technologies including swarm intelligence algorithms and other evolutionary algorithm paradigms are considered as the widely accepted descriptors for mobile sustainable networks virtualization, optimization, and automation. To deal with the emerging impacts on mobile communication networks, this book discusses about the state-of-the research works on developing a sustainable design and their implementation in mobile networks. With the advent of evolutionary computation algorithms, this book contributes varied research chapters to develop a new perspective on mobile sustainable networks.
Evolutionary Computing in Advanced Manufacturing (Wiley-Scrivener #73)
by Manoj Tiwari Jenny A. HardingThis cutting-edge book covers emerging, evolutionary and nature inspired optimization techniques in the field of advanced manufacturing. The complexity of real life advanced manufacturing problems often cannot be solved by traditional engineering or computational methods. Hence, in recent years researchers and practitioners have proposed and developed new strands of advanced, intelligent techniques and methodologies. Evolutionary computing approaches are introduced in the context of a wide range of manufacturing activities, and through the examination of practical problems and their solutions, readers will gain confidence to apply these powerful computing solutions. The initial chapters introduce and discuss the well established evolutionary algorithm, to help readers to understand the basic building blocks and steps required to successfully implement their own solutions to real life advanced manufacturing problems. In the later chapters, modified and improved versions of evolutionary algorithms are discussed. The book concludes with appendices which provide general descriptions of several evolutionary algorithms.
Evolutionary Critical Theory and Its Role in Public Affairs
by Charles Federick Abel Arthur Jay SementelliThis work addresses one of the most central and timely subjects in Public Administration - how to make sense of critical theory and especially how to assess its implications for everyday practice.
Evolutionary Data Clustering: Algorithms and Applications (Algorithms for Intelligent Systems)
by Ibrahim Aljarah Hossam Faris Seyedali MirjaliliThis book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances (Studies in Computational Intelligence #1070)
by Yanan Sun Gary G. Yen Mengjie ZhangThis book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
Evolutionary Dynamics of Forests under Climate Change
by Claire G. WilliamsFocusing on the example of the Lost Pines forest of Texas, this book contextualises the present-day conservation of the Lost Pines within its wealth of historical and geological records. This in turn presents a realistic example for examining evolutionary dynamics models and how they can guide management of temperate pine forests under the uncertainty of future climate change. Synthesising knowledge from many scholarly disciplines, and presenting the latest knowledge on how temperate forests respond to climate change, the book provides insight into how resource professionals actually solve complex multi-layered problems. A useful aid for forest management professionals and for advanced students and professionals in ecology, the book is a valuable resource for researchers and professionals, which can also be used as a classroom exercise for spatial imaging, testing virtual simulations and developing field-based research questions.
Evolutionary Games with Sociophysics: Analysis of Traffic Flow and Epidemics (Evolutionary Economics and Social Complexity Science #17)
by Jun TanimotoRecent applications of evolutionary game theory in the merging fields of the mathematical and social sciences are brilliantly portrayed in this book, which highlights social physics and shows how the approach can help to quantitatively model complex human–environmental–social systems.First, readers are introduced to the fundamentals of evolutionary game theory. The two-player, two-strategy game, or the 2 × 2 game, is presented as an archetype to help understand the difficulty of cooperating for survival against defection in common social contexts. Subsequently, the book explains the theoretical background of the multi-player, two-strategy game, which may be more widely applicable than the 2 × 2 game for social dilemmas. The latest applications of 2 × 2 games are also discussed to explore how integrated reciprocity mechanisms can solve social dilemmas.In turn, the book describes two practical areas in which evolutionary game theory has been applied. The first concerns traffic flow analysis. In conventional interpretations, traffic flow can be understood by means of fluid dynamics, in which the flow of vehicles is evaluated as a continuum body. Such a simple idea, however, does not work well in reality, particularly if a driver’s decision-making process is considered. Various dilemmas involve complex structures that depend primarily on traffic density, a revelation that should help establish a practical solution for reducing traffic congestion.Second, the book provides keen insights into how powerful evolutionary game theory can be in the context of epidemiology. Both approaches, quasi-analytical and multi-agent simulation, can clarify how an infectious disease such as seasonal influenza spreads across a complex social network, which is significantly affected by the public attitude toward vaccination. A methodology is proposed for the optimum design of a public vaccination policy incorporating subsidies to efficiently increase vaccination coverage while minimizing the social cost.
Evolutionary Humanoid Robotics
by Malachy EatonThis book examines how two distinct strands of research on autonomous robots, evolutionary robotics and humanoid robot research, are converging. The book will be valuable for researchers and postgraduate students working in the areas of evolutionary robotics and bio-inspired computing.
Evolutionary Intelligence: How Technology Will Make Us Smarter
by W. Russell NeumanA surprising vision of how human intelligence will coevolve with digital technology and revolutionize how we think and behave.It is natural for us to fear artificial intelligence. But does Siri really want to kill us? Perhaps we are falling into the trap of projecting human traits onto the machines we might build. In Evolutionary Intelligence, Neuman offers a surprisingly positive vision in which computational intelligence compensates for the well-recognized limits of human judgment, improves decision making, and actually increases our agency. In artful, accessible, and adventurous prose, Neuman takes the reader on an exciting, fast-paced ride, all the while making a convincing case about a revolution in computationally augmented human intelligence.Neuman argues that, just as the wheel made us mobile and machines made us stronger, the migration of artificial intelligence from room-sized computers to laptops to our watches, smart glasses, and even smart contact lenses will transform day-to-day human decision making. If intelligence is the capacity to match means with ends, then augmented intelligence can offer the ability to adapt to changing environments as we face the ultimate challenge of long-term survival.Tapping into a global interest in technology&’s potential impacts on society, economics, and culture, Evolutionary Intelligence demonstrates that our future depends on our ability to computationally compensate for the limitations of a human cognitive system that has only recently graduated from hunting and gathering.
Evolutionary Intelligence for Healthcare Applications (AIoT - Artificial Intelligence of Things)
by T. Ananth Kumar R. Rajmohan M. Pavithra S. BalamuruganThis book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis. Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.
Evolutionary Large-Scale Multi-Objective Optimization and Applications
by Xingyi Zhang Yaochu Jin Ye Tian Ran ChengTackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book’s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
Evolutionary Machine Learning Techniques: Algorithms and Applications (Algorithms for Intelligent Systems)
by Seyedali Mirjalili Hossam Faris Ibrahim AljarahThis book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Evolutionary Manufacturing, Design and Operational Practices for Resource and Environmental Sustainability
by Sachindra Kumar Rout Sardar M.N. Islam Kamalakanta Muduli Sunil Sarangi Aezeden MohamedThis book highlights the important use of digital technologies and the latest developments in mechanical and industrial engineering to enhance environmental and resource sustainability. Sustainable Development Goals (SDGs) have as their overarching objective the reduction or eradication of a wide range of global problems, including, but not limited to poverty, climate change, environmental degradation, and inequality. Digital technologies (DTs) have the potential to be exploited to meet the goals associated with the circular economy (CE) and sustainable development. Additive manufacturing (AM), cyber-physical systems (CPS), and blockchain technology are examples of DT-enabled technologies that are helpful for businesses that seek to shift to a circular economic model. With the remanufacturing of products, applications that make use of virtual reality and augmented reality, in addition to the Internet of Things, simplify the construction of strategic decision models that reduce time and expense while simultaneously increasing productivity. In addition, the utilization of big data analytics helps businesses discover previously undisclosed trends and unlock numerous opportunities for environmental and resource sustainability. Employing analytics makes it feasible to collect helpful information regarding the socio-environmental impact of a product, as well as consumption factors over the entirety of a product’s life cycle. This book contains 44 comprehensive chapters and is divided into five parts. Part 1 delves deeply into sustainable operational practices and supply chain management. The impact that digital technology-enabled operational techniques have on product life cycles is investigated, as well as the design of efficient remanufacturing processes, environmentally friendly logistics and warehousing practices, sustainable designs for distributed energy supply systems, and efficient recycling procedures. Part 2 provides a perspective on advanced materials and developments for sustainable manufacturing. The chapters in this section address sustainable material development and its application in the circular economy concept. Included here is an in-depth exploration of cutting-edge technology for synthesis, processing, fabrication, process optimization, testing, and performance evaluation of advanced materials. Part 3 covers sustainable manufacturing practices and looks at the problems faced by the industry when using digital technologies in their operations, as well as the possible benefits. Part 4 examines sustainable innovation in mechanical design. It addresses all aspects of mechanical design that contribute to sustainable innovation for nation-building. Part 5 delves into heat transfer and fluid flow concepts for sustainable product development and applications. The chapters explain how to construct sustainable energy systems by reducing the total amount of energy that is utilized, enhancing the efficiency of the process of energy conversion, and making use of sources of energy that are renewable. Audience This book has a wide audience in academic institutions and engineers in a variety of manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems: A Case Studies Approach (Mechanical Engineering Series)
by Biplab Das JagadishThis book presents insights into the thermal performance of solar thermal collectors using both computational and experimental modeling. It consists of various computational and experimental case studies conducted by the authors on the solar thermal collector system. The authors begin by developing thermal modeling using a case study that shows the effect of different governing parameters. A few more experimental cases studies follow that highlight the energy, exergy, and environmental performance of the solar thermal collector system and to examine the performance of a modified solar collector system, illustrating performance improvement techniques. Finally, application of different evolutionary optimization techniques such as soft computing and evolutionary methods, like fuzzy techniques, MCDM methods like fuzzy logic based expert system (FLDS), Artificial Neural Network (ANN), Grey relational analysis (GRA), Entropy-Jaya algorithm, Entropy-VIKOR etc. are employed.
Evolutionary Multi-Objective System Design: Theory and Applications (Chapman & Hall/CRC Computer and Information Science Series)
by Nadia Nedjah, Luiza De Macedo Mourelle and Heitor Silverio LopesReal-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems. Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers’ preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems: Embrittlement of stainless steel coated electrodes Learning fuzzy rules from imbalanced datasets Combining multi-objective evolutionary algorithms with collective intelligence Fuzzy gain scheduling control Smart placement of roadside units in vehicular networks Combining multi-objective evolutionary algorithms with quasi-simplex local search Design of robust substitution boxes Protein structure prediction problem Core assignment for efficient network-on-chip-based system design