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Nature Inspired Optimization Algorithms With Java
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Book Synopsis Nature-Inspired Optimization Algorithms with Java by : Shashank Jain
Download or read book Nature-Inspired Optimization Algorithms with Java written by Shashank Jain and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. You will: 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.
Book Synopsis Nature-Inspired Optimization Algorithms by : Xin-She Yang
Download or read book Nature-Inspired Optimization Algorithms written by Xin-She Yang and published by Elsevier. This book was released on 2014-02-17 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-Inspired Optimization Algorithms provides a systematic 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 well-chosen 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, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding as well as practical implementation hints Provides a step-by-step introduction to each algorithm
Book Synopsis Nature-Inspired Optimization Algorithms by : Vasuki A
Download or read book Nature-Inspired Optimization Algorithms written by Vasuki A and published by CRC Press. This book was released on 2020-05-31 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-Inspired Optimization Algorithms, a comprehensive work on the most popular optimization algorithms based on nature, starts with an overview of optimization going from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of optimization techniques, providing us with simple solutions to complex problems in an effective and adaptive manner. The study of the intelligent survival strategies of animals, birds, and insects in a hostile and ever-changing environment has led to the development of techniques emulating their behavior. This book is a lucid description of fifteen important existing optimization algorithms based on swarm intelligence and superior in performance. It is a valuable resource for engineers, researchers, faculty, and students who are devising optimum solutions to any type of problem ranging from computer science to economics and covering diverse areas that require maximizing output and minimizing resources. This is the crux of all optimization algorithms. Features: Detailed description of the algorithms along with pseudocode and flowchart Easy translation to program code that is also readily available in Mathworks website for some of the algorithms Simple examples demonstrating the optimization strategies are provided to enhance understanding Standard applications and benchmark datasets for testing and validating the algorithms are included This book is a reference for undergraduate and post-graduate students. It will be useful to faculty members teaching optimization. It is also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature-inspired optimization algorithms are unconventional, and this makes them more efficient than their traditional counterparts.
Book Synopsis Introduction to Nature-Inspired Optimization by : George Lindfield
Download or read book Introduction to Nature-Inspired Optimization written by George Lindfield and published by Academic Press. This book was released on 2017-08-10 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Handbook of Nature-Inspired Optimization Algorithms: The State of the Art by : Ali Mohamed
Download or read book Handbook of Nature-Inspired Optimization Algorithms: The State of the Art written by Ali Mohamed and published by Springer Nature. This book was released on 2022-08-31 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
Book Synopsis Design and Applications of Nature Inspired Optimization by : Dipti Singh
Download or read book Design and Applications of Nature Inspired Optimization written by Dipti Singh and published by Springer Nature. This book was released on 2023-01-02 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a detailed information of various real-life applications from various fields using nature inspired optimization techniques. These techniques are proven to be efficient and robust in many difficult problems in literature. The authors provide detailed information about real-life problems and how various nature inspired optimizations are applied to solve these problems. The authors discuss techniques such as Biogeography Based Optimization, Glow Swarm Optimization, Elephant herd Optimization Algorithm, Cuckoo Search Algorithm, Ant Colony Optimization, and Grey Wolf Optimization etc. These algorithms are applied to a wide range of problems from the field of engineering, finance, medicinal etc. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in nature inspired optimization, inspiring women and men, girls and boys to enter and apply themselves to the field.
Book Synopsis Frontiers in Nature-Inspired Industrial Optimization by : Mahdi Khosravy
Download or read book Frontiers in Nature-Inspired Industrial Optimization written by Mahdi Khosravy and published by Springer Nature. This book was released on 2021-08-06 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a collection of recent applications of nature inspired optimization in industrial fields. Different optimization techniques have been deployed, and different problems have been effectively analyzed. The valuable contributions from researchers focus on three ultimate goals (i) improving the accuracy of these techniques, (ii) achieving higher speed and lower computational complexity, and (iii) working on their proposed applications. The book is helpful for active researchers and practitioners in the field.
Book Synopsis Nature-Inspired Optimization Algorithms by : Aditya Khamparia
Download or read book Nature-Inspired Optimization Algorithms written by Aditya Khamparia and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-02-08 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Nature-Inspired Algorithms for Optimisation by : Raymond Chiong
Download or read book Nature-Inspired Algorithms for Optimisation written by Raymond Chiong and published by Springer Science & Business Media. This book was released on 2009-04-28 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
Book Synopsis Nature-Inspired Optimization Algorithms with Java by : Shashank Jain
Download or read book Nature-Inspired Optimization Algorithms with Java written by Shashank Jain and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. You will: 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.
Book Synopsis Nature-Inspired Algorithms and Applied Optimization by : Xin-She Yang
Download or read book Nature-Inspired Algorithms and Applied Optimization written by Xin-She Yang and published by Springer. This book was released on 2017-10-08 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.
Book Synopsis Nature-inspired Methods for Metaheuristics Optimization by :
Download or read book Nature-inspired Methods for Metaheuristics Optimization written by and published by . This book was released on 2020 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.
Book Synopsis Harmony Search and Nature Inspired Optimization Algorithms by : Neha Yadav
Download or read book Harmony Search and Nature Inspired Optimization Algorithms written by Neha Yadav and published by Springer. This book was released on 2018-08-23 with total page 1209 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Solving Constrained Single Objective Real-parameter Optimization Problems by : Ali Mohamed
Download or read book Solving Constrained Single Objective Real-parameter Optimization Problems written by Ali Mohamed and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nature-inspired Optimizers by : Seyedali Mirjalili
Download or read book Nature-inspired Optimizers written by Seyedali Mirjalili and published by . This book was released on 2020 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Optimization by Nature-Inspired Algorithms by : Omid Bozorg-Haddad
Download or read book Advanced Optimization by Nature-Inspired Algorithms written by Omid Bozorg-Haddad and published by Springer. This book was released on 2017-06-30 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.
Book Synopsis Nature Inspired Metaheuristic Optimization Algorithms and Applications by : Samuel Gaxiola
Download or read book Nature Inspired Metaheuristic Optimization Algorithms and Applications written by Samuel Gaxiola and published by . This book was released on 2020 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regarding optimization methods, there is much inspiration to be found in nature and biology. There has been much effort in recent years to further and develop new methods and algorithms for approaching optimization problems, with the inspiration for the logic for these algorithms coming from biology and nature in general. Broadly speaking, there are three categories of algorithms with respect to the logical structures. Swarm intelligence algorithms operate with a logic that utilizes multiple instances in the search space to find an optimal solution by emulating the way lifeforms search for food or shelter in swarms. Evolutionary algorithms operate in such a way that an initial set of instances, referred to as the initial population, will have certain characteristics used to measure fitness, which are then passed on to the subsequent "offspring" instances in each iteration of the algorithm until some predetermined stop condition is met. Population-based stochastic algorithms utilize an element of randomness when creating instances and analyzing the search space, which make the logic less susceptible to converging to an undesirable solution. Because these categories are so broad in their definitions and characteristics, there are many algorithms that fall within multiple categories. In this work, three algorithms are analyzed and characterized. The Invasive Weed Optimization (IWO) algorithm utilizes the concept of unwanted weeds in a garden spreading and serves as the basis for a population-based stochastic algorithm. The Imperialist Competitive Algorithm (ICA) emulates the observed relationships between imperialist and colony states in history and utilizes a logic that encapsulates some features of evolutionary algorithms while also being stochastic and population based. A hybrid algorithm comprised of population-based stochastic characteristics taken from the IWO and swarm intelligence from the Particle Swarm Optimization (PSO) algorithm provides a more comprehensive and robust method of traversing the search space, benefiting from the strengths of both algorithms. These algorithms were characterized using benchmark test functions. They were then applied to an antenna pattern optimization problem and comparisons between performances were analyzed. This work outlines the individual strengths and weaknesses of each algorithm while demonstrating that they outperform the Genetic Algorithm (GA) with respect to computation time and results for the antenna optimization problem.
