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With the emergence of the data economy, information has become integral to business excellence. Every enterprise, irrespective of its domain of interest, carries and processes a lot of data in their day-to-day activities. Converting massive datasets into insightful information plays an important role in developing better business solutions. Data intelligence and its analysis pose several challenges in data representation, building knowledge systems, issue resolution and predictive systems for trend analysis and decisionmaking. The data available could be of any modality, especially when data is associated with healthcare, biomedical, finance, retail, cybersecurity, networking, supply chain management, manufacturing, etc. The optimization of such systems is therefore crucial to leveraging the best outcomes and conclusions. To this end, AI-based nature-inspired optimization methods or approximation-based optimization methods are becoming very powerful. Notable metaheuristics include genetic algorithms, differential evolution, ant colony optimization, particle swarm optimization, artificial bee colony, grey wolf optimizer, political optimizer, cohort intelligence and league championship algorithm. This book provides a systematic discussion of AI-based metaheuristics application in a wide range of areas, including big data intelligence and predictive analytics, enterprise analytics, graph optimization algorithms, machine learning and ensemble learning, computer vision enterprise practices and data benchmarking.
Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. Provides a unique opportunity to present the work on the state-of-the-art of metaheuristics approach in the area of big data processing developing automated and intelligent models Explains different, feasible applications and case studies where cognitive computing can be successfully implemented in big data analytics using metaheuristics algorithms Provides a snapshot of the latest advances in the contribution of metaheuristics frameworks in cognitive big data applications to solve optimization problems
In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
This book deals with the management and valuation of energy storage in electric power grids, highlighting the interest of storage systems in grid applications and developing management methodologies based on artificial intelligence tools. The authors highlight the importance of storing electrical energy, in the context of sustainable development, in "smart cities" and "smart transportation", and discuss multiple services that storing electrical energy can bring. Methodological tools are provided to build an energy management system storage following a generic approach. These tools are based on causal formalisms, artificial intelligence and explicit optimization techniques and are presented throughout the book in connection with concrete case studies.
"This book is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing, providing readers with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization"--Provided by publisher.
The amount of data shared and stored on the web and other document repositories is steadily on the rise. Unfortunately, this growth increases inefficiencies and difficulties when trying to find the most relevant and up-to-date information due to unstructured data. Advanced Metaheuristic Methods in Big Data Retrieval and Analytics examines metaheuristic techniques as an important alternative model for solving complex problems that are not treatable by deterministic methods. Recent studies suggest that IR and biomimicry can be used together for several application problems in big data and internet of things, especially when conventional methods would be too expensive or difficult to implement. Featuring coverage on a broad range of topics such as ontology, plagiarism detection, and machine learning, this book is ideally designed for engineers, graduate students, IT professionals, and academicians seeking an overview of new trends in information retrieval in big data.
This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code. Extra Material can be downloaded at http://extras.springer.com/978-3-319-68117-7.
This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.
Developments in metaheuristics continue to advance computation beyond its traditional methods. With groundwork built on multidisciplinary research findings; metaheuristics, algorithms, and optimization approaches uses memory manipulations in order to take full advantage of strategic level problem solving. Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches provides insight on the latest advances and analysis of technologies in metaheuristics computing. Offering widespread coverage on topics such as genetic algorithms, differential evolution, and ant colony optimization, this book aims to be a forum researchers, practitioners, and students who wish to learn and apply metaheuristic computing.
Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.