LEADER 05518nam 2200673 450 001 996426342003316 005 20200520144314.0 010 $a0-12-416745-4 035 $a(CKB)3710000000088571 035 $a(EBL)1637335 035 $a(SSID)ssj0001164536 035 $a(PQKBManifestationID)11677235 035 $a(PQKBTitleCode)TC0001164536 035 $a(PQKBWorkID)11181628 035 $a(PQKB)11108649 035 $a(MiAaPQ)EBC1637335 035 $a(CaSebORM)9780124167438 035 $a(Au-PeEL)EBL1637335 035 $a(CaPaEBR)ebr10839265 035 $a(CaONFJC)MIL604993 035 $a(OCoLC)880315949 035 $a(EXLCZ)993710000000088571 100 $a20140306h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNature-inspired optimization algorithms /$fXin-She Yang 205 $aFirst edition. 210 1$aLondon, [England] ;$aWaltham, [Massachusetts] :$cElsevier,$d2014. 210 4$dİ2014 215 $a1 online resource (277 p.) 300 $aDescription based upon print version of record. 311 $a0-12-416743-8 320 $aIncludes bibliographical references. 327 $aHalf Title; Title Page; Copyright; Contents; Preface; 1 Introduction to Algorithms; 1.1 What is an Algorithm?; 1.2 Newton's Method; 1.3 Optimization; 1.3.1 Gradient-Based Algorithms; 1.3.2 Hill Climbing with Random Restart; 1.4 Search for Optimality; 1.5 No-Free-Lunch Theorems; 1.5.1 NFL Theorems; 1.5.2 Choice of Algorithms; 1.6 Nature-Inspired Metaheuristics; 1.7 A Brief History of Metaheuristics; References; 2 Analysis of Algorithms; 2.1 Introduction; 2.2 Analysis of Optimization Algorithms; 2.2.1 Algorithm as an Iterative Process; 2.2.2 An Ideal Algorithm?; 2.2.3 A Self-Organization System 327 $a2.2.4 Exploration and Exploitation2.2.5 Evolutionary Operators; 2.3 Nature-Inspired Algorithms; 2.3.1 Simulated Annealing; 2.3.2 Genetic Algorithms; 2.3.3 Differential Evolution; 2.3.4 Ant and Bee Algorithms; 2.3.5 Particle Swarm Optimization; 2.3.6 The Firefly Algorithm; 2.3.7 Cuckoo Search; 2.3.8 The Bat Algorithm; 2.3.9 Harmony Search; 2.3.10 The Flower Algorithm; 2.3.11 Other Algorithms; 2.4 Parameter Tuning and Parameter Control; 2.4.1 Parameter Tuning; 2.4.2 Hyperoptimization; 2.4.3 Multiobjective View; 2.4.4 Parameter Control; 2.5 Discussions; 2.6 Summary; References 327 $a3 Random Walks and Optimization3.1 Random Variables; 3.2 Isotropic Random Walks; 3.3 Le?vy Distribution and Le?vy Flights; 3.4 Optimization as Markov Chains; 3.4.1 Markov Chain; 3.4.2 Optimization as a Markov Chain; 3.5 Step Sizes and Search Efficiency; 3.5.1 Step Sizes, Stopping Criteria, and Efficiency; 3.5.2 Why Le?vy Flights are More Efficient; 3.6 Modality and Intermittent Search Strategy; 3.7 Importance of Randomization; 3.7.1 Ways to Carry Out Random Walks; 3.7.2 Importance of Initialization; 3.7.3 Importance Sampling; 3.7.4 Low-Discrepancy Sequences; 3.8 Eagle Strategy 327 $a3.8.1 Basic Ideas of Eagle Strategy3.8.2 Why Eagle Strategy is So Efficient; References; 4 Simulated Annealing; 4.1 Annealing and Boltzmann Distribution; 4.2 Parameters; 4.3 SA Algorithm; 4.4 Unconstrained Optimization; 4.5 Basic Convergence Properties; 4.6 SA Behavior in Practice; 4.7 Stochastic Tunneling; References; 5 Genetic Algorithms; 5.1 Introduction; 5.2 Genetic Algorithms; 5.3 Role of Genetic Operators; 5.4 Choice of Parameters; 5.5 GA Variants; 5.6 Schema Theorem; 5.7 Convergence Analysis; References; 6 Differential Evolution; 6.1 Introduction; 6.2 Differential Evolution 327 $a6.3 Variants6.4 Choice of Parameters; 6.5 Convergence Analysis; 6.6 Implementation; References; 7 Particle Swarm Optimization; 7.1 Swarm Intelligence; 7.2 PSO Algorithm; 7.3 Accelerated PSO; 7.4 Implementation; 7.5 Convergence Analysis; 7.5.1 Dynamical System; 7.5.2 Markov Chain Approach; 7.6 Binary PSO; References; 8 Firefly Algorithms; 8.1 The Firefly Algorithm; 8.1.1 Firefly Behavior; 8.1.2 Standard Firefly Algorithm; 8.1.3 Variations of Light Intensity and Attractiveness; 8.1.4 Controlling Randomization; 8.2 Algorithm Analysis; 8.2.1 Scalings and Limiting Cases 327 $a8.2.2 Attraction and Diffusion 330 $aNature-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, paramete 606 $aComputer algorithms 606 $aParallel processing (Electronic computers) 606 $aElectronic data processing$xDistributed processing 606 $aArtificial intelligence 608 $aElectronic books. 615 0$aComputer algorithms. 615 0$aParallel processing (Electronic computers) 615 0$aElectronic data processing$xDistributed processing. 615 0$aArtificial intelligence. 676 $a006.3 700 $aYang$b Xin-She$0781375 801 0$bMiAaPQ 906 $aBOOK 912 $a996426342003316 996 $aNature-inspired optimization algorithms$92351981 997 $aUNISA