Nature Inspired Optimization Algorithms

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

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  • Author : Xin-She Yang
  • Publisher : Elsevier
  • Pages : 300 pages
  • ISBN : 0124167454
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNature Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms
  • Author : Xin-She Yang
  • Publisher : Elsevier
  • Release : 17 February 2014
GET THIS BOOKNature-Inspired Optimization Algorithms

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

Nature-Inspired Optimization Algorithms with Java

Nature-Inspired Optimization Algorithms with Java
  • Author : Shashank Jain
  • Publisher : Apress
  • Release : 12 December 2021
GET THIS BOOKNature-Inspired Optimization Algorithms with Java

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

Nature-inspired Metaheuristic Algorithms

Nature-inspired Metaheuristic Algorithms
  • Author : Xin-She Yang
  • Publisher : Luniver Press
  • Release : 04 July 2022
GET THIS BOOKNature-inspired Metaheuristic Algorithms

Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm

Introduction to Nature-Inspired Optimization

Introduction to Nature-Inspired Optimization
  • Author : George Lindfield,John Penny
  • Publisher : Academic Press
  • Release : 10 August 2017
GET THIS BOOKIntroduction to Nature-Inspired Optimization

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.

Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms
  • Author : Vasuki A
  • Publisher : CRC Press
  • Release : 31 May 2020
GET THIS BOOKNature-Inspired Optimization Algorithms

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

Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms
  • Author : Aditya Khamparia,Ashish Khanna,Nhu Gia Nguyen,Bao Le Nguyen
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 08 February 2021
GET THIS BOOKNature-Inspired Optimization Algorithms

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,

Nature-Inspired Algorithms and Applied Optimization

Nature-Inspired Algorithms and Applied Optimization
  • Author : Xin-She Yang
  • Publisher : Springer
  • Release : 08 October 2017
GET THIS BOOKNature-Inspired Algorithms and Applied Optimization

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

Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications

Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications
  • Author : Modestus O. Okwu,Lagouge K. Tartibu
  • Publisher : Springer Nature
  • Release : 13 November 2020
GET THIS BOOKMetaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm,

Nature Inspired Optimization Techniques for Image Processing Applications

Nature Inspired Optimization Techniques for Image Processing Applications
  • Author : Jude Hemanth,Valentina Emilia Balas
  • Publisher : Springer
  • Release : 19 September 2018
GET THIS BOOKNature Inspired Optimization Techniques for Image Processing Applications

This book provides a platform for exploring nature-inspired optimization techniques in the context of imaging applications. Optimization has become part and parcel of all computational vision applications, and since the amount of data used in these applications is vast, the need for optimization techniques has increased exponentially. These accuracy and complexity are a major area of concern when it comes to practical applications. However, these optimization techniques have not yet been fully explored in the context of imaging applications. By

Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems

Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems
  • Author : Radu-Emil Precup,Radu-Codrut David
  • Publisher : Butterworth-Heinemann
  • Release : 15 May 2019
GET THIS BOOKNature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems

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

Nature-Inspired Computing and Optimization

Nature-Inspired Computing and Optimization
  • Author : Srikanta Patnaik,Xin-She Yang,Kazumi Nakamatsu
  • Publisher : Springer
  • Release : 07 March 2017
GET THIS BOOKNature-Inspired Computing and Optimization

The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from

Nature-Inspired Methods for Metaheuristics Optimization

Nature-Inspired Methods for Metaheuristics Optimization
  • Author : Fouad Bennis,Rajib Kumar Bhattacharjya
  • Publisher : Springer Nature
  • Release : 17 January 2020
GET THIS BOOKNature-Inspired Methods for Metaheuristics Optimization

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,

Mathematical Foundations of Nature-Inspired Algorithms

Mathematical Foundations of Nature-Inspired Algorithms
  • Author : Xin-She Yang,Xing-Shi He
  • Publisher : Springer
  • Release : 08 May 2019
GET THIS BOOKMathematical Foundations of Nature-Inspired Algorithms

This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and

Advanced Optimization by Nature-Inspired Algorithms

Advanced Optimization by Nature-Inspired Algorithms
  • Author : Omid Bozorg-Haddad
  • Publisher : Springer
  • Release : 30 June 2017
GET THIS BOOKAdvanced Optimization by Nature-Inspired Algorithms

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

Harmony Search and Nature Inspired Optimization Algorithms

Harmony Search and Nature Inspired Optimization Algorithms
  • Author : Neha Yadav,Anupam Yadav,Jagdish Chand Bansal,Kusum Deep,Joong Hoon Kim
  • Publisher : Springer
  • Release : 23 August 2018
GET THIS BOOKHarmony Search and Nature Inspired Optimization Algorithms

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.