MARC Record from marc_columbia

LEADER: 08288cam a2200733 i 4500
001 14923388
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008 200607s2020 flu ob 000 0 eng d
035 $a(OCoLC)on1156990453
035 $a(NNC)14923388
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019 $a1157078033
020 $a9781000076646$q(electronic bk.)
020 $a1000076644$q(electronic bk.)
020 $a9780429289071$q(electronic bk.)
020 $a0429289073$q(electronic bk.)
020 $a9781000076608$q(electronic bk. ;$qPDF)
020 $a1000076601$q(electronic bk. ;$qPDF)
020 $a9781000076622$q(electronic bk. ;$qMobipocket)
020 $a1000076628$q(electronic bk. ;$qMobipocket)
020 $z0367503298
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035 $a(OCoLC)1156990453$z(OCoLC)1157078033
037 $a9780429289071$bTaylor & Francis
050 4 $aQA76.9.N37
072 7 $aCOM$x012040$2bisacsh
072 7 $aCOM$x059000$2bisacsh
072 7 $aMAT$x004000$2bisacsh
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082 04 $a006.38$223
049 $aZCUA
100 1 $aA, Vasuki.
245 10 $aNature-inspired optimization algorithms /$cVasuki A.
260 $aBoca Raton, FL :$bChapman and Hall/CRC,$c2020.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
520 $aNature-Inspired Optimization Algorithms, a comprehensivework on the most popular optimization algorithms based on nature, starts with an overview of optimizationgoing 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 led to the development of techniques emulating their behavior. This book is a lucid description of fifteen important existing optimization algorithms based on swarm intelligence and superior in performance. It is a valuable resource for engineers, researchers, faculty, and students who are devising optimum solutions to any type of problem rangingfrom computer science to economics andcovering diverse areas that require maximizing output and minimizing resources. This is the crux of all optimization algorithms. Features: Detailed description of the algorithms along with pseudocode and flowchart Easy translation to program code that is also readily available in Mathworks website for some of the algorithms Simple examples demonstrating the optimization strategies are provided to enhance understanding Standard applications and benchmark datasets for testing and validating the algorithms are included This book is a reference for undergraduate and post-graduate students. It will be useful to faculty members teaching optimization. It is also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature-inspired optimization algorithms are unconventional, and this makes them more efficient than their traditional counterparts.
504 $aIncludes bibliographical references.
545 0 $aDr. Vasuki A is currently working as Professor in the Department of Mechatronics Engineering at Kumaraguru College of Technology, Coimbatore, India. She has more than 27 years of teaching, research and academic administration experience. She has completed B.E in Electronics and Communication Engineering from PSG College of Technology in 1989. She has completed her postgraduate degree M.E Applied Electronics from Coimbatore Institute of Technology in 1991. She has done her Ph. D in Image Compression from PSG College of Technology under Anna University Chennai in 2010. Her research interests are Signal Processing, Image Processing, Communication and Optimization. She has published 3 Book Chapters, 38 National and International Journal papers and 60 National and International Conference papers. She has guided 30 PG projects and 50 UG projects. She is an approved Research Supervisor under Anna University Chennai and is currently guiding 9 research scholars.
505 0 $aCover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author -- 1 Introduction -- 1.1 Introduction -- 1.2 Fundamentals of Optimization -- 1.3 Types of Optimization Problems -- 1.4 Examples of Optimization -- 1.5 Formulation of Optimization Problem -- 1.6 Classification of Optimization Algorithms -- 1.7 Traveling Salesman Problem and Knapsack Problem -- 1.8 Summary -- 2 Classical Optimization Methods -- 2.1 Introduction -- 2.2 Mathematical Model of Optimization -- 2.3 Linear Programming -- 2.3.1 Simplex Method -- 2.3.2 Revised Simplex Method
505 8 $a2.3.3 Kamarkar's Method -- 2.3.4 Duality Theorem -- 2.3.5 Decomposition Principle -- 2.3.6 Transportation Problem -- 2.4 Non-Linear Programming -- 2.4.1 Quadratic Programming -- 2.4.2 Geometric Programming -- 2.5 Dynamic Programming -- 2.6 Integer Programming -- 2.7 Stochastic Programming -- 2.8 Lagrange Multiplier Method -- 2.9 Summary -- References -- 3 Nature-Inspired Algorithms -- 3.1 Introduction -- 3.2 Traditional versus Nature-Inspired Algorithms -- 3.3 Bioinspired Algorithms -- 3.4 Swarm Intelligence -- 3.5 Metaheuristics -- 3.6 Diversification and Intensification
505 8 $a3.7 No Free Lunch Theorem -- 3.8 Parameter Tuning and Control -- 3.9 Algorithm -- 3.10 Pseudocode -- 3.11 Summary -- References -- 4 Genetic Algorithm -- 4.1 Introduction -- 4.2 Basics of Genetic Algorithm -- 4.3 Genetic Operators -- 4.4 Example of GA -- 4.5 Algorithm -- 4.6 Pseudocode -- 4.7 Schema Theory -- 4.8 Prisoner's Dilemma Problem -- 4.9 Variants and Hybrids of GA -- 4.10 Summary -- References -- 5 Genetic Programming -- 5.1 Introduction -- 5.2 Basics of Genetic Programming -- 5.3 Data Structures for Genetic Programming -- 5.4 Binary Tree Traversals -- 5.5 Genetic Programming Operators
505 8 $a5.6 Genetic Programming Algorithm -- 5.7 Pseudocode -- 5.8 Variants of the Algorithm -- 5.9 Summary -- References -- 6 Particle Swarm Optimization -- 6.1 Introduction -- 6.2 Swarm Behavior -- 6.3 Particle Swarm Optimization -- 6.3.1 Algorithm -- 6.3.2 Pseudocode -- 6.4 Variants of the Algorithm -- 6.5 Summary -- References -- 7 Differential Evolution -- 7.1 Introduction -- 7.2 Differential Evolution -- 7.2.1 Algorithm -- 7.2.2 Pseudocode -- 7.3 Variants of the Algorithm -- 7.4 Summary -- References -- 8 Ant Colony Optimization -- 8.1 Introduction -- 8.2 Ant Colony Characteristics
505 8 $a8.3 Ant Colony Optimization -- 8.3.1 Traveling Salesman Problem -- 8.3.2 Algorithm -- 8.3.3 Pseudocode -- 8.4 Variants of the Algorithm -- 8.5 Summary -- References -- 9 Bee Colony Optimization -- 9.1 Introduction -- 9.2 Honey Bee Characteristics -- 9.3 Bee Colony Optimization -- 9.3.1 Algorithm -- 9.3.2 Pseudocode -- 9.4 Variants of the Algorithm -- 9.5 Summary -- References -- 10 Fish School Search Algorithm -- 10.1 Introduction -- 10.2 Fish School Behavior -- 10.3 Fish School Search Optimization -- 10.3.1 Algorithm -- 10.3.2 Pseudocode -- 10.4 Variants and Applications -- 10.5 Summary
650 0 $aNature-inspired algorithms.
650 0 $aNatural computation.
650 0 $aMathematical optimization.
650 6 $aAlgorithmes inspirés par la nature.
650 6 $aCalcul naturel.
650 6 $aOptimisation mathématique.
650 7 $aCOMPUTERS$xComputer Graphics$xGame Programming & Design.$2bisacsh
650 7 $aCOMPUTERS$xComputer Engineering.$2bisacsh
650 7 $aMATHEMATICS$xArithmetic.$2bisacsh
650 7 $aMathematical optimization.$2fast$0(OCoLC)fst01012099
650 7 $aNatural computation.$2fast$0(OCoLC)fst01745866
650 7 $aNature-inspired algorithms.$2fast$0(OCoLC)fst01986501
655 4 $aElectronic books.
776 08 $iPrint version:$z0367503298$z9780367503291$w(OCoLC)1155323077
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio14923388$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS