Nature Inspired Computation and Swarm Intelligence

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

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  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Pages : 442 pages
  • ISBN : 0128197145
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNature Inspired Computation and Swarm Intelligence

Nature-Inspired Computation and Swarm Intelligence

Nature-Inspired Computation and Swarm Intelligence
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release : 24 April 2020
GET THIS BOOKNature-Inspired Computation and Swarm Intelligence

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Xin-She Yang,Mehmet Karamanoglu
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Xin-She Yang,Zhihua Cui,Renbin Xiao,Amir Hossein Gandomi,Mehmet Karamanoglu
  • Publisher : Newnes
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

Nature-Inspired Computation in Data Mining and Machine Learning

Nature-Inspired Computation in Data Mining and Machine Learning
  • Author : Xin-She Yang,Xing-Shi He
  • Publisher : Springer Nature
  • Release : 03 September 2019
GET THIS BOOKNature-Inspired Computation in Data Mining and Machine Learning

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Shichang Sun,Hongbo Liu
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

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

Recent Advances in Swarm Intelligence and Evolutionary Computation

Recent Advances in Swarm Intelligence and Evolutionary Computation
  • Author : Xin-She Yang
  • Publisher : Springer
  • Release : 27 December 2014
GET THIS BOOKRecent Advances in Swarm Intelligence and Evolutionary Computation

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Sean Walton,Oubay Hassan,Kenneth Morgan,M. Rowan Brown
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

Nature-Inspired Computation in Engineering

Nature-Inspired Computation in Engineering
  • Author : Xin-She Yang
  • Publisher : Springer
  • Release : 19 March 2016
GET THIS BOOKNature-Inspired Computation in Engineering

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

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization
  • Author : Javier Del Ser Lorente,Eneko Osaba
  • Publisher : BoD – Books on Demand
  • Release : 18 July 2018
GET THIS BOOKNature-inspired Methods for Stochastic, Robust and Dynamic Optimization

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Priti Srinivas Sajja,Rajendra Akerkar
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

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

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation
  • Author : Gilang Kusuma Jati,Ruli Manurung,null Suyanto
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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 ‘

Swarm Intelligence and Bio-Inspired Computation

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
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOKSwarm Intelligence and Bio-Inspired Computation

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

Nature-Inspired Computing

Nature-Inspired Computing
  • Author : Nazmul H. Siddique,Hojjat Adeli
  • Publisher : CRC Press
  • Release : 19 May 2017
GET THIS BOOKNature-Inspired Computing

Nature-Inspired Computing: Physics and Chemistry-Based Algorithms provides a comprehensive introduction to the methodologies and algorithms in nature-inspired computing, with an emphasis on applications to real-life engineering problems. The research interest for Nature-inspired Computing has grown considerably exploring different phenomena observed in nature and basic principles of physics, chemistry, and biology. The discipline has reached a mature stage and the field has been well-established. This endeavour is another attempt at investigation into various computational schemes inspired from nature, which are presented