Swarm Intelligence and Bio Inspired Computation

Produk Detail:
  • Author : Xin-She Yang
  • Publisher : Newnes
  • Pages : 450 pages
  • ISBN : 0124051774
  • Rating : 5/5 from 1 reviews
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Download or Read online Swarm Intelligence and Bio Inspired Computation full in PDF, ePub and kindle. this book written by Xin-She Yang and published by Newnes which was released on 16 May 2013 with total page 450 pages. We cannot guarantee that Swarm Intelligence and Bio Inspired Computation book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

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 BOOK Swarm 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

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 BOOK Swarm 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 : M.P. Saka,E. Doğan,Ibrahim Aydogdu
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Swarm intelligence refers to collective intelligence. Biologists and natural scientist have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. In spite of the fact that these insects are unsophisticated individually, they make wonders as a swarm by interaction with each other and their environment. In last two decades, the behaviors of various swarms that are used in

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Simon Fong
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Data mining has evolved from methods of simple statistical analysis to complex pattern recognition in the past decades. During the progression, the data mining algorithms are modified or extended in order to overcome some specific problems. This chapter discusses about the prospects of improving data mining algorithms by integrating bio-inspired optimization, which has lately captivated much of researchers’ attention. In particular, high dimensionality and the unavailability of the whole data set (as in stream mining) in the training data have

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 BOOK Swarm 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

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Raha Imanirad,Julian Scott Yeomans
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different approaches to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time that the supporting decision models are constructed. There are invariably unmodeled design issues, not apparent at the time of model

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Zhihua Cui,Xingjuan Cai
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Artificial plant optimization algorithm (APOA) is a novel evolutionary strategy inspired by tree’s growing process. In this chapter, the methodologies of prototypal APOA and its updated version are illustrated. First, the primary framework is introduced by accounting for photosynthesis and phototropism phenomena. Since some important factors are ignored during mimicking branch’s growing, the optimization is sometimes misleading and time-consuming. Therefore, the standard version is developed by adding geotropism mechanism and apical dominance operator. The quality of the proposed

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Tamás Varga,András Király,János Abonyi
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of Particle Swarm Optimization (PSO) algorithm but classical gradient calculation cannot be applied to stochastic and uncertain systems. In these situations Monte-Carlo (MC) simulation can be applied to determine the gradient. We developed a memory-based algorithm where instead of generating and evaluating new simulated samples the stored and

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Maximos A. Kaliakatsos-Papakostas,Andreas Floros,Michael N. Vrahatis
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies

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 BOOK Swarm 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

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Renbin Xiao
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

In view of labor division in swarm intelligence, a new research paradigm of “problem-oriented approach to swarm intelligence” is constructed. The key to the success of such an approach is to grasp the features of problem objects sufficiently. At first, the labor division behaviors of ant colonies are discoursed and some descriptions of ant colony’s labor division models are given. Taking three practical problems as the backgrounds, the corresponding modeling and simulation approaches to ant colony’s labor division

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Momin Jamil,Xin-She Yang,Hans-Jürgen Zepernick
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Test functions are important to validate and compare the performance of various optimization algorithms. In previous years, there have been many test or benchmark functions reported in the literature. However, there is no standard list or set of benchmark functions with diverse properties that algorithms may be tested upon. On the other hand, any new optimization algorithm should be tested by a diverse range of test or benchmark functions so as to see if it can solve certain types of

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Iztok Fister,Xin-She Yang,Janez Brest,Iztok Jr. Fister
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

The “firefly algorithm” (FFA) is a modern metaheuristic algorithm, inspired by the behavior of fireflies. This algorithm and its variants have been successfully applied to many continuous optimization problems. This work analyzes the performance of the FFA when solving combinatorial optimization problems. In order to improve the results, the original FFA is extended and improved for self-adaptation of control parameters, and thus more directly balancing between exploration and exploitation in the search process of fireflies. We use a new population

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 BOOK Swarm 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

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation
  • Author : Jonas Krause,Jelson Cordeiro,Rafael Stubs Parpinelli,Heitor Silvério Lopes
  • Publisher : Elsevier Inc. Chapters
  • Release : 16 May 2013
GET THIS BOOK Swarm Intelligence and Bio Inspired Computation

Most swarm intelligence algorithms were devised for continuous optimization problems. However, they have been adapted for discrete optimization as well with applications in different domains. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that motivated the creation of each swarm algorithm is introduced, and later, the discretization and encoding methods are used to adapt each algorithm for discrete problems. Methods are compared for