Skip to main content

Swarm Intelligence And Bio Inspired Computation

In Order to Read Online or Download Swarm Intelligence And Bio Inspired Computation Full eBooks in PDF, EPUB, Tuebl and Mobi you need to create a Free account. Get any books you like and read everywhere you want. Fast Download Speed ~ Commercial & Ad Free. We cannot guarantee that every book is in the library!

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Xin-She Yang,Mehmet Karamanoglu
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068876
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Tamás Varga,András Király,János Abonyi
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128069058
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 shared former function evaluations of the particles are sampled to estimate the gradients by local weighted least squares regression. The performance of the resulted regional gradient-based PSO is verified by several benchmark problems and in a complex application example where optimal reorder points of a supply chain are determined.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Maximos A. Kaliakatsos-Papakostas,Andreas Floros,Michael N. Vrahatis
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068965
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Raha Imanirad,Julian Scott Yeomans
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128069007
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modeled objective(s) but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modeling-to-generate-alternatives (MGA). This chapter provides a synopsis of various MGA techniques and demonstrates how biologically inspired MGA algorithms are particularly efficient at creating multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy and efficiency of these MGA methods are demonstrated using a number of case studies.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Momin Jamil,Hans-Jürgen Zepernick
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068892
Language : En, Es, Fr & De

GET BOOK

Book Description :

Random walks play an important and central role in metaheuristic and stochastic optimization algorithms. The two key components of the search process in metaheuristic algorithms (MAs) are intensification and diversification. The overall efficiency of a metaheuristic optimization algorithm depends on a sound balance between these two components. In MAs, exploration is achieved by randomization in combination with a deterministic procedure. In this way, the newly generated solutions are distributed as diversely as possible in the problem search space. In most of the MAs, randomization is realized using a uniform or Gaussian distribution. However, this is not the only way to achieve randomization. In recent years, the use of Lévy distribution has emerged as an alternative to uniform or Gaussian distributions. In view of these details, this chapter focuses on using Lévy flights (LFs) in the context of global optimization. A survey of the most important MAs using LFs to achieve intensification and diversification for solving global optimization problems is presented. The different components and concepts of Lévy-flight-based MAs are discussed and their similarities and differences are analyzed.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Iztok Fister,Xin-She Yang,Janez Brest,Iztok Jr. Fister
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068906
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 model to increase the selection pressure, and the next generation selects only the fittest between a parent and an offspring population. As a result, the proposed memetic self-adaptive FFA (MSA-FFA) is compared with other well-known graph coloring algorithms such as Tabucol, the hybrid evolutionary algorithm, and an evolutionary algorithm with stepwise adaptation of weights. Various experiments have been conducted on a huge set of randomly generated graphs. The results of these experiments show that the results of the MSA-FFA are comparable with other tested algorithms.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Gilang Kusuma Jati,Ruli Manurung,null Suyanto
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 012806899X
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Simon Fong
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 012806904X
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 known to be two major challenges. We demonstrated that these two challenges, through two small examples such as K-means clustering and time-series classification, can be overcome by integrating data mining and bio-inspired algorithms.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Amir Hossein Gandomi,Amir Hossein Alavi,Siamak Talatahari
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128069015
Language : En, Es, Fr & De

GET BOOK

Book Description :

A new metaheuristic optimization algorithm, called krill herd (KH), has been recently proposed by Gandomi and Alavi. In this study, KH is introduced for structural optimization. For more verification, KH is subsequently applied to three design problems reported in the literature. The performance of the KH algorithm is further compared with various algorithms representative of the state of the art in the area. The comparisons show that the results obtained by KH can be better than the best solutions obtained by the existing methods in these three case studies.

Nature Inspired Computation and Swarm Intelligence

Nature Inspired Computation and Swarm Intelligence Book
Author : Xin-She Yang
Publisher : Academic Press
Release : 2020-04-24
ISBN : 0128197145
Language : En, Es, Fr & De

GET BOOK

Book Description :

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

Recent Advances in Swarm Intelligence and Evolutionary Computation

Recent Advances in Swarm Intelligence and Evolutionary Computation Book
Author : Xin-She Yang
Publisher : Springer
Release : 2014-12-27
ISBN : 331913826X
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Bio Inspired Computational Intelligence and Applications

Bio Inspired Computational Intelligence and Applications Book
Author : Dr. Kang Li,Minrui Fei,George W. Irwin,Shiwei Ma
Publisher : Springer Science & Business Media
Release : 2007-08-28
ISBN : 9783540747680
Language : En, Es, Fr & De

GET BOOK

Book Description :

The two volumes LNCS 4688 and LNBI 4689 constitute the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2007, held in Shanghai, China, in September 2007. The 147 revised full papers were carefully reviewed and selected from 1383 submissions. The 84 papers of LNCS 4688 are organized in topical sections on advanced neural network theory, algorithms and application, advanced evolutionary computing theory, algorithms and application, ant colonies and particle swarm optimization and application, fuzzy, neural, fuzzy-neuro hybrids and application, intelligent modeling, monitoring, and control of complex nonlinear systems, as well as biomedical signal processing, imaging and visualization.

Bio Inspired Computation and Applications in Image Processing

Bio Inspired Computation and Applications in Image Processing Book
Author : Xin-She Yang,João Paulo Papa
Publisher : Academic Press
Release : 2016-08-09
ISBN : 012804537X
Language : En, Es, Fr & De

GET BOOK

Book Description :

Bio-Inspired Computation and Applications in Image Processing summarizes the latest developments in bio-inspired computation in image processing, focusing on nature-inspired algorithms that are linked with deep learning, such as ant colony optimization, particle swarm optimization, and bat and firefly algorithms that have recently emerged in the field. In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish new research avenues to pursue. Reviews the latest developments in bio-inspired computation in image processing Focuses on the introduction and analysis of the key bio-inspired methods and techniques Combines theory with real-world applications in image processing Helps solve complex problems in image and signal processing Contains a diverse range of self-contained case studies in real-world applications

Proceedings of Seventh International Conference on Bio Inspired Computing Theories and Applications BIC TA 2012

Proceedings of Seventh International Conference on Bio Inspired Computing  Theories and Applications  BIC TA 2012  Book
Author : Jagdish C. Bansal,Pramod Singh,Kusum Deep,Millie Pant,Atulya K. Nagar
Publisher : Springer Science & Business Media
Release : 2012-12-04
ISBN : 8132210387
Language : En, Es, Fr & De

GET BOOK

Book Description :

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.

Bio Inspired Computation in Telecommunications

Bio Inspired Computation in Telecommunications Book
Author : Xin-She Yang,Su Fong Chien,T.O. Ting
Publisher : Morgan Kaufmann
Release : 2015-02-11
ISBN : 0128017430
Language : En, Es, Fr & De

GET BOOK

Book Description :

Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

Proceedings of The Eighth International Conference on Bio Inspired Computing Theories and Applications BIC TA 2013

Proceedings of The Eighth International Conference on Bio Inspired Computing  Theories and Applications  BIC TA   2013 Book
Author : Zhixiang Yin,Linqiang Pan,Xianwen Fang
Publisher : Springer Science & Business Media
Release : 2013-10-22
ISBN : 3642375022
Language : En, Es, Fr & De

GET BOOK

Book Description :

International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) is one of the flagship conferences on Bio-Computing, bringing together the world’s leading scientists from different areas of Natural Computing. Since 2006, the conferences have taken place at Wuhan (2006), Zhengzhou (2007), Adelaide (2008), Beijing (2009), Liverpool & Changsha (2010), Malaysia (2011) and India (2012). Following the successes of previous events, the 8th conference is organized and hosted by Anhui University of Science and Technology in China. This conference aims to provide a high-level international forum that researchers with different backgrounds and who are working in the related areas can use to present their latest results and exchange ideas. Additionally, the growing trend in Emergent Systems has resulted in the inclusion of two other closely related fields in the BIC-TA 2013 event, namely Complex Systems and Computational Neuroscience. These proceedings are intended for researchers in the fields of Membrane Computing, Evolutionary Computing and Genetic Algorithms, DNA and Molecular Computing, Biological Computing, Swarm Intelligence, Autonomy-Oriented Computing, Cellular and Molecular Automata, Complex Systems, etc. Professor Zhixiang Yin is the Dean of the School of Science, Anhui University of Science & Technology, China. Professor Linqiang Pan is the head of the research group of Natural Computing at Huazhong University of Science and Technology, Wuhan, China. Professor Xianwen Fang also works at the Anhui University of Science & Technology.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Priti Srinivas Sajja,Rajendra Akerkar
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068981
Language : En, Es, Fr & De

GET BOOK

Book Description :

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.

Computational Vision and Bio Inspired Computing

Computational Vision and Bio Inspired Computing Book
Author : S. Smys,João Manuel R. S. Tavares,Valentina Emilia Balas,Abdullah M. Iliyasu
Publisher : Springer Nature
Release : 2020-01-06
ISBN : 3030372189
Language : En, Es, Fr & De

GET BOOK

Book Description :

This proceedings book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. Due to the rapid advances in the emerging information, communication and computing technologies, the Internet of Things, cloud and edge computing, and artificial intelligence play a significant role in the computational vision context. In recent years, computational vision has contributed to enhancing the methods of controlling the operations in biological systems, like ant colony optimization, neural networks, and immune systems. Moreover, the ability of computational vision to process a large number of data streams by implementing new computing paradigms has been demonstrated in numerous studies incorporating computational techniques in the emerging bio-inspired models. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization, and big data modeling and management, that make use of effectual computing processes in the bio-inspired systems. As such it contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems, and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : Jonas Krause,Jelson Cordeiro,Rafael Stubs Parpinelli,Heitor Silvério Lopes
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068930
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 different classes of problems and a critical analysis is provided, pointing to future trends.

Swarm Intelligence and Bio Inspired Computation

Swarm Intelligence and Bio Inspired Computation Book
Author : M.P. Saka,E. Doğan,Ibrahim Aydogdu
Publisher : Elsevier Inc. Chapters
Release : 2013-05-16
ISBN : 0128068884
Language : En, Es, Fr & De

GET BOOK

Book Description :

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 finding preys or mating are simulated into a numerical optimization technique. In this chapter, eight different swarm intelligence–based algorithms are summarized and their working steps are listed. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. Two optimization problems taken from the literature are solved by all these eight algorithms and their performance are compared. It is noticed that most of the swarm intelligence–based algorithms are simple and robust techniques that determine the optimum solution of optimization problems efficiently without requiring much of a mathematical struggling.