# Nature-Inspired Computation and Swarm Intelligence

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## Nature Inspired Computation and Swarm Intelligence

Author | : Xin-She Yang |

Publsiher | : Academic Press |

Total Pages | : 442 |

Release | : 2020-04-24 |

ISBN 10 | : 0128197145 |

ISBN 13 | : 9780128197141 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computation and Swarm Intelligence Book Review:**

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

## Swarm Intelligence and Bio Inspired Computation

Author | : Xin-She Yang,Mehmet Karamanoglu |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0128068876 |

ISBN 13 | : 9780128068878 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.

## Nature Inspired Computation in Data Mining and Machine Learning

Author | : Xin-She Yang,Xing-Shi He |

Publsiher | : Springer Nature |

Total Pages | : 273 |

Release | : 2019-09-03 |

ISBN 10 | : 3030285537 |

ISBN 13 | : 9783030285531 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computation in Data Mining and Machine Learning Book Review:**

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

## Swarm Intelligence and Bio Inspired Computation

Author | : Xin-She Yang,Zhihua Cui,Renbin Xiao,Amir Hossein Gandomi,Mehmet Karamanoglu |

Publsiher | : Newnes |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0124051774 |

ISBN 13 | : 9780124051775 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

## Swarm Intelligence and Bio Inspired Computation

Author | : Shichang Sun,Hongbo Liu |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0128068922 |

ISBN 13 | : 9780128068922 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

In this chapter, we present the convergence analysis and applications of particle swarm optimization algorithm. Although it is difficult to analyze the convergence of this algorithm, we discuss its convergence based on its iterated function system and probabilistic theory. The dynamic trajectory of the particle is described based on single individual. We also attempt to theoretically prove that the swarm algorithm converges with a probability of 1 toward the global optimal. We apply the algorithms to solve the scheduling problem and peer-to-peer neighbor selection problem. This chapter is also concerned to employ the nature-inspired optimization methods in machine learning. We introduce the swarm algorithm to reoptimize hidden Markov models.

## Nature Inspired Computation and Swarm Intelligence

Author | : Xin-She Yang |

Publsiher | : Academic Press |

Total Pages | : 442 |

Release | : 2020-04-09 |

ISBN 10 | : 0128226099 |

ISBN 13 | : 9780128226094 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computation and Swarm Intelligence Book Review:**

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

## Swarm Intelligence and Bio Inspired Computation

Author | : Xin-She Yang,Zhihua Cui,Renbin Xiao,Mehmet Karamanoglu,Amir Hossein Gandomi |

Publsiher | : Elsevier |

Total Pages | : 450 |

Release | : 2013-06-01 |

ISBN 10 | : 9781493301362 |

ISBN 13 | : 1493301365 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

## Recent Advances in Swarm Intelligence and Evolutionary Computation

Author | : Xin-She Yang |

Publsiher | : Springer |

Total Pages | : 300 |

Release | : 2014-12-27 |

ISBN 10 | : 331913826X |

ISBN 13 | : 9783319138268 |

Language | : EN, FR, DE, ES & NL |

**Recent Advances in Swarm Intelligence and Evolutionary Computation Book Review:**

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.

## Nature Inspired Computation in Engineering

Author | : Xin-She Yang |

Publsiher | : Springer |

Total Pages | : 276 |

Release | : 2016-03-19 |

ISBN 10 | : 3319302353 |

ISBN 13 | : 9783319302355 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computation in Engineering Book Review:**

This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.

## Nature Inspired Computing Concepts Methodologies Tools and Applications

Author | : Management Association, Information Resources |

Publsiher | : IGI Global |

Total Pages | : 1780 |

Release | : 2016-07-26 |

ISBN 10 | : 1522507892 |

ISBN 13 | : 9781522507895 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computing Concepts Methodologies Tools and Applications Book Review:**

As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

## Nature Inspired Optimization Algorithms

Author | : Xin-She Yang |

Publsiher | : Elsevier |

Total Pages | : 300 |

Release | : 2014-02-17 |

ISBN 10 | : 0124167454 |

ISBN 13 | : 9780124167452 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Optimization Algorithms Book Review:**

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

## Swarm Intelligence and Bio Inspired Computation

Author | : Gilang Kusuma Jati,Ruli Manurung,null Suyanto |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 012806899X |

ISBN 13 | : 9780128068991 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

The “firefly algorithm” (FA) is a nature-inspired technique originally designed for solving continuous optimization problems. There are several existing approaches that apply FA also as a basis for solving discrete optimization problems, in particular the “traveling salesman problem” (TSP). In this chapter, we present a new movement scheme called edge-based movement, an operation which guarantees that a candidate solution more closely resembles another one. This leads to a more FA-like behavior of the algorithm. We investigate the performance of the ‘evolutionary discrete firefly algorithm” when using this new edge-based movement and compare it against previous methods. Computer simulations show that the new movement scheme produces slightly better accuracy with much faster average time. The average speedup factor is 14.06 times.

## Nature inspired Methods for Stochastic Robust and Dynamic Optimization

Author | : Javier Del Ser Lorente,Eneko Osaba |

Publsiher | : BoD – Books on Demand |

Total Pages | : 70 |

Release | : 2018-07-18 |

ISBN 10 | : 1789233283 |

ISBN 13 | : 9781789233285 |

Language | : EN, FR, DE, ES & NL |

**Nature inspired Methods for Stochastic Robust and Dynamic Optimization Book Review:**

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems.

## Swarm Intelligence and Bio Inspired Computation

Author | : Priti Srinivas Sajja,Rajendra Akerkar |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0128068981 |

ISBN 13 | : 9780128068984 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

Bio-inspired models have taken inspiration from the nature to solve challenging problems in an intelligent manner. Major aims of such bio-inspired models of computation are to propose new unconventional computing architectures and novel problem solving paradigms. Computing models such as artificial neural network (ANN), genetic algorithm (GA), and swarm intelligence (SI) are major constituent models of the bio-inspired approach. Applications of these models are ubiquitous and hence proposed to be applied for Semantic Web. The chapter discusses fundamentals of these bio-inspired constituents along with some heuristic that can be used to design and implement these constituents and briefly surveys recent applications of these models for the Semantic Web. The study shows that the objective of the Semantic Web is better met with such approach and the Web can be accessed in more human-oriented way. At the end, a generic framework for web content filtering based on neuro-fuzzy approach is presented. By considering online webpages and fuzzy user profile, the proposed system classifies the webpages into vague categories using a neural network.

## Nature Inspired Computation

Author | : Mario D'Acunto |

Publsiher | : Unknown |

Total Pages | : 194 |

Release | : 2015 |

ISBN 10 | : 9781634824767 |

ISBN 13 | : 1634824768 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computation Book Review:**

Nature inspired computation is an old idea, first proposed in the early fifties by Alan Turing, one of the founders of computer science. Turing suggested computational models of pattern formation in living systems based on systems of coupled reaction-diffusion equations giving rise to spatial patterns due to self-organization of substances in chemical concentrations. Since the pioneering work by Turing, many optimization algorithms stimulated by real-world features have gained great popularity and impact, thanks to their efficiency in solving nonlinear design problems. Nature-inspired computation has permeated into almost all areas of sciences, engineering and industries, from data mining to optimization, from computational intelligence to signal processing, from image analysis and vision systems to industrial applications. The book provides an introductory tour of the most popular nature inspired computational strategies. The book is subdivided in two parts, briefly describing the inspiration and motivation of natural processes and phenomena, main players, design principles, the scope of each branch, current trends and open problems. In the first section, attention is focused on Artificial and Spiking Neural Networks (Chapter 2), Evolutionary and Genetic Algorithms (Chapter 3), and Swarm Intelligence algorithms (Chapter 4). In the second section, we present the emergent knowledge and technologies in Multiscale Nature processes (Chapter 5), Quantum Computing and Quantum Cryptography (Chapter 6), Encryption and Secure Communication system (Chapter 7), Image processing and Vision systems (Chapter 8), and finally on Nanophotonics Information (Chapter 9).

## Swarm Intelligence and Bio Inspired Computation

Author | : Sean Walton,Oubay Hassan,Kenneth Morgan,M. Rowan Brown |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0128068973 |

ISBN 13 | : 9780128068977 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

The cuckoo search is a relatively new gradient free optimization algorithm, which has been growing in popularity. The algorithm aims to replicate the particularly aggressive breeding behavior of cuckoos and it makes use of the Lévy flight, which is an efficient search pattern. In this chapter, the original development of the cuckoo search is discussed and a number of modifications that have been made to the basic procedure are compared. A number of applications of the cuckoo search are described and some possible future developments of the cuckoo search algorithm are summarized.

## Swarm Intelligence and Bio Inspired Computation

Author | : Rodrigo Yuji Mizobe Nakamura,Luís Augusto Martins Pereira,Douglas Rodrigues,Kelton Augusto Pontara Costa,João Paulo Papa,Xin-She Yang |

Publsiher | : Elsevier Inc. Chapters |

Total Pages | : 450 |

Release | : 2013-05-16 |

ISBN 10 | : 0128068957 |

ISBN 13 | : 9780128068953 |

Language | : EN, FR, DE, ES & NL |

**Swarm Intelligence and Bio Inspired Computation Book Review:**

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques.

## Metaheuristic Optimization Nature Inspired Algorithms Swarm and Computational Intelligence Theory and Applications

Author | : Modestus O. Okwu,Lagouge K. Tartibu |

Publsiher | : Springer Nature |

Total Pages | : 192 |

Release | : 2020-11-13 |

ISBN 10 | : 3030611116 |

ISBN 13 | : 9783030611118 |

Language | : EN, FR, DE, ES & NL |

**Metaheuristic Optimization Nature Inspired Algorithms Swarm and Computational Intelligence Theory and Applications Book Review:**

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, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

## Frontier Applications of Nature Inspired Computation

Author | : Mahdi Khosravy,Neeraj Gupta,Nilesh Patel,Tomonobu Senjyu |

Publsiher | : Springer Nature |

Total Pages | : 389 |

Release | : 2020-03-11 |

ISBN 10 | : 9811521336 |

ISBN 13 | : 9789811521331 |

Language | : EN, FR, DE, ES & NL |

**Frontier Applications of Nature Inspired Computation Book Review:**

This book addresses the frontier advances in the theory and application of nature-inspired optimization techniques, including solving the quadratic assignment problem, prediction in nature-inspired dynamic optimization, the lion algorithm and its applications, optimizing the operation scheduling of microgrids, PID controllers for two-legged robots, optimizing crane operating times, planning electrical energy distribution systems, automatic design and evaluation of classification pipelines, and optimizing wind-energy power generation plants. The book also presents a variety of nature-inspired methods and illustrates methods of adapting these to said applications. Nature-inspired computation, developed by mimicking natural phenomena, makes a significant contribution toward the solution of non-convex optimization problems that normal mathematical optimizers fail to solve. As such, a wide range of nature-inspired computing approaches has been used in multidisciplinary engineering applications. Written by researchers and developers from a variety of fields, this book presents the latest findings, novel techniques and pioneering applications.

## Nature Inspired Computing and Optimization

Author | : Srikanta Patnaik,Xin-She Yang,Kazumi Nakamatsu |

Publsiher | : Springer |

Total Pages | : 494 |

Release | : 2017-03-07 |

ISBN 10 | : 3319509209 |

ISBN 13 | : 9783319509204 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Computing and Optimization Book Review:**

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 topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.