Nature Inspired Optimization Algorithms And Soft Computing
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Nature-inspired Optimization Algorithms and Soft Computing by Rajeev Arya,Sangeeta Singh,Maheshwari P. Singh,Brijesh R. Iyer,Venkat N. Gudivada Pdf
This edited book reviews the intertwining disciplines of nature-inspired optimization algorithms and bio-inspired soft-computing for real world applications, with the interaction between metaheuristics with complex systems. The authors present methods and techniques in IoT, image processing, smart manufacturing and healthcare.
Nature-Inspired Optimization Algorithms by Xin-She Yang Pdf
Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding and practical implementation hints Presents a step-by-step introduction to each algorithm Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications
Harmony Search and Nature Inspired Optimization Algorithms by Neha Yadav,Anupam Yadav,Jagdish Chand Bansal,Kusum Deep,Joong Hoon Kim Pdf
The book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Fourth International Conference on Harmony Search, Soft Computing and Applications held at BML Munjal University, Gurgaon, India on February 7–9, 2018. It consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms.
Advances in Nature-Inspired Computing and Applications by Shishir Kumar Shandilya,Smita Shandilya,Atulya K. Nagar Pdf
This book contains research contributions from leading global scholars in nature-inspired computing. It includes comprehensive coverage of each respective topic, while also highlighting recent and future trends. The contributions provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application. This book has focus on the current researches while highlighting the empirical results along with theoretical concepts to provide a comprehensive reference for students, researchers, scholars, professionals and practitioners in the field of Advanced Artificial Intelligence, Nature-Inspired Algorithms and Soft Computing.
Handbook of Research on Soft Computing and Nature-Inspired Algorithms by Shandilya, Shishir K.,Shandilya, Smita,Deep, Kusum,Nagar, Atulya K. Pdf
Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly research on applications of nature-inspired computing and soft computational systems. Featuring comprehensive coverage on a range of topics and perspectives such as swarm intelligence, speech recognition, and electromagnetic problem solving, this publication is ideally designed for students, researchers, scholars, professionals, and practitioners seeking current research on the advanced workings of intelligence in computing systems.
Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems by Radu-Emil Precup,Radu-Codrut David Pdf
Nature-inspired Optimization Algorithms for Fuzzy Controlled Servo Systems suits the general need of a book that explains the major issues to fuzzy control in servo systems without any solid mathematical prerequisite. In addition, pertinent information on nature-inspired optimization algorithms is offered. The book is intended to rapidly make intelligible notions of fuzzy set theory and fuzzy control to readers with limited experience. The attractive analysis and design methodologies dedicated to fuzzy controllers are accompanied by applications to servo systems and case studies in fuzzy controlled servo systems are organized in a special chapter of this book, and allow simple implementations of low-cost automation solutions. The theoretical approaches presented throughout the book are validated by the illustration of digital simulation results and real-time experimental results as well. This book aims at a large category of audience including graduate students, engineers (designers, practitioners and researchers), and everyone who faces challenging control problems. Gives a merge between classical and modern approaches to fuzzy control Presents in a unified structure from the point of view of a control engineer the essential aspects regarding fuzzy control in servo systems Makes intelligible notions of fuzzy set theory and fuzzy control to readers with limited experience
Soft Computing Applications in Optimization, Control, and Recognition by Patricia Melin,Oscar Castillo Pdf
Soft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. This book describes the application of soft computing techniques to intelligent control, pattern recognition, and optimization problems. The book is organized in four main parts. The first part deals with nature-inspired optimization methods and their applications. Papers included in this part propose new models for achieving intelligent optimization in different application areas. The second part discusses hybrid intelligent systems for achieving control. Papers included in this part make use of nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers for different kind of applications. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems. The fourth part discusses new theoretical concepts and methods for the application of soft computing to many different areas, such as natural language processing, clustering and optimization.
Nature-Inspired Optimizers by Seyedali Mirjalili,Jin Song Dong,Andrew Lewis Pdf
This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.
Nature-Inspired Computation and Swarm Intelligence by Xin-She Yang Pdf
Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence. Introduces nature-inspired algorithms and their fundamentals, including: particle swarm optimization, bat algorithm, cuckoo search, firefly algorithm, flower pollination algorithm, differential evolution and genetic algorithms as well as multi-objective optimization algorithms and others Provides a theoretical foundation and analyses of algorithms, including: statistical theory and Markov chain theory on the convergence and stability of algorithms, dynamical system theory, benchmarking of optimization, no-free-lunch theorems, and a generalized mathematical framework Includes a diversity of case studies of real-world applications: feature selection, clustering and classification, tuning of restricted Boltzmann machines, travelling salesman problem, classification of white blood cells, music generation by artificial intelligence, swarm robots, neural networks, engineering designs and others
Nature-Inspired Algorithms and Applications by S. Balamurugan,Anupriya Jain,Sachin Sharma,Dinesh Goyal,Sonia Duggal,Seema Sharma Pdf
NATURE-INSPIRED ALGORITHMS AND APPLICATIONS The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. Among the subjects of the 12 chapters are: A novel method based on TRIZ to map real-world problems to nature problems Applications of cuckoo search algorithm for optimization problems Performance analysis of nature-inspired algorithms in breast cancer diagnosis Nature-inspired computation in data mining Hybrid bat-genetic algorithm–based novel optimal wavelet filter for compression of image data Efficiency of finding best solutions through ant colony optimization techniques Applications of hybridized algorithms and novel algorithms in the field of machine learning. Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.
Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms by Dash, Sujata,Tripathy, B.K.,Rahman, Atta ur Pdf
The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.
Nature-Inspired Optimization Algorithms by Aditya Khamparia,Ashish Khanna,Nhu Gia Nguyen,Bao Le Nguyen Pdf
This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations
Nature-Inspired Optimization Algorithms with Java by Shashank Jain Pdf
Gain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java. You'll start with an introduction to the hidden algorithms that nature uses and find the approximate solutions to optimization problems. You'll then see how living creatures such as fish and birds are able to perform computation to solve specific optimization tasks. This book also covers various nature-inspired algorithms by reviewing code examples for each one followed by crisp and clear explanations of the algorithm using Java code. You'll examine the use of each algorithm in specific industry scenarios such as fleet scheduling in supply chain management, and shop floor management in industrial and manufacturing applications. Nature-Inspired Optimization Algorithms with Java is your pathway to understanding a variety of optimization problems that exist in various industries and domains and it will develop an ability to apply nature-inspired algorithms to find approximate solutions to them. What You'll Learn Study optimization and its problems Examine nature-inspired algorithms such as Particle Swarm, Gray wolf, etc. See how nature-inspired algorithms are being used to solve optimization problems Use Java for solving the different nature-inspired algorithms with real-world examples Who This Book Is For Software developers/architects who are looking to hone their skills in area of problem solving related to optimization with Java.
Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization by Patricia Melin,Oscar Castillo,Janusz Kacprzyk Pdf
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.
Introduction to Nature-Inspired Optimization by George Lindfield,John Penny Pdf
Introduction to Nature-Inspired Optimization brings together many of the innovative mathematical methods for non-linear optimization that have their origins in the way various species behave in order to optimize their chances of survival. The book describes each method, examines their strengths and weaknesses, and where appropriate, provides the MATLAB code to give practical insight into the detailed structure of these methods and how they work. Nature-inspired algorithms emulate processes that are found in the natural world, spurring interest for optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant colony optimization and the cuckoo search algorithm, among others. This book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization. Applies concepts in nature and biology to develop new algorithms for nonlinear optimization Offers working MATLAB® programs for the major algorithms described, applying them to a range of problems Provides useful comparative studies of the algorithms, highlighting their strengths and weaknesses Discusses the current state-of-the-field and indicates possible areas of future development