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Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 434 pages, kõrgus x laius: 235x155 mm, kaal: 8041 g, 40 Illustrations, color; 28 Illustrations, black and white; XXI, 434 p. 68 illus., 40 illus. in color., 1 Hardback
  • Ilmumisaeg: 02-Aug-2016
  • Kirjastus: Birkhauser Verlag AG
  • ISBN-10: 3319411918
  • ISBN-13: 9783319411910
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Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 434 pages, kõrgus x laius: 235x155 mm, kaal: 8041 g, 40 Illustrations, color; 28 Illustrations, black and white; XXI, 434 p. 68 illus., 40 illus. in color., 1 Hardback
  • Ilmumisaeg: 02-Aug-2016
  • Kirjastus: Birkhauser Verlag AG
  • ISBN-10: 3319411918
  • ISBN-13: 9783319411910
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319411910.html
Märksõnad:
This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.  An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmon

y search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.  Sea rch and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

Preface.- Introduction.- Simulated Annealing.- Optimization by Recurrent Neural Networks.- Genetic Algorithms and Genetic Programming.- Evolutionary Strategies.- Differential Evolution.- Estimation of Distribution Algorithms.- Mimetic Algorithms.- Topics in EAs.- Particle Swarm Optimization.- Artificial Immune Systems.- Ant Colony Optimization.- Tabu Search and Scatter Search.- Bee Metaheuristics.- Harmony Search.- Biomolecular Computing.- Quantum Computing.- Other Heuristics-Inspired Optimization Methods.- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations.- Multiobjective Optimization.- Appendix 1: Discrete Benchmark Functions.- Appendix 2: Test Functions.- Index.

Arvustused

The book under review contains large amount of precisely selected topics covering various aspects and design techniques related to efficient metaheuristic algorithms for searching and optimization. is intended primarily as a textbook for graduate students specializing in engineering and computer science. Besides being very useful as a valuable resource for post-docs and researchers working in these areas, it may as well be used by those who are interested in search and optimization methods in general. (Vladimír Lacko, zbMATH, 1351.90002, 2017)

1 Introduction
1(28)
1.1 Computation Inspired by Nature
1(2)
1.2 Biological Processes
3(2)
1.3 Evolution Versus Learning
5(1)
1.4 Swarm Intelligence
6(3)
1.4.1 Group Behaviors
7(1)
1.4.2 Foraging Theory
8(1)
1.5 Heuristics, Metaheuristics, and Hyper-Heuristics
9(2)
1.6 Optimization
11(9)
1.6.1 Lagrange Multiplier Method
12(1)
1.6.2 Direction-Based Search and Simplex Search
13(1)
1.6.3 Discrete Optimization Problems
14(2)
1.6.4 P, NP, NP-Hard, and NP-Complete
16(1)
1.6.5 Multiobjective Optimization Problem
17(2)
1.6.6 Robust Optimization
19(1)
1.7 Performance Indicators
20(2)
1.8 No Free Lunch Theorem
22(1)
1.9 Outline of the Book
23(6)
References
25(4)
2 Simulated Annealing
29(8)
2.1 Introduction
29(1)
2.2 Basic Simulated Annealing
30(3)
2.3 Variants of Simulated Annealing
33(4)
References
35(2)
3 Genetic Algorithms
37(34)
3.1 Introduction to Evolutionary Computation
37(2)
3.1.1 Evolutionary Algorithms Versus Simulated Annealing
39(1)
3.2 Terminologies of Evolutionary Computation
39(3)
3.3 Encoding/Decoding
42(1)
3.4 Selection/Reproduction
43(3)
3.5 Crossover
46(2)
3.6 Mutation
48(1)
3.7 Noncanonical Genetic Operators
49(2)
3.8 Exploitation Versus Exploration
51(4)
3.9 Two-Dimensional Genetic Algorithms
55(1)
3.10 Real-Coded Genetic Algorithms
56(4)
3.11 Genetic Algorithms for Sequence Optimization
60(11)
References
64(7)
4 Genetic Programming
71(12)
4.1 Introduction
71(1)
4.2 Syntax Trees
72(3)
4.3 Causes of Bloat
75(1)
4.4 Bloat Control
76(2)
4.4.1 Limiting on Program Size
77(1)
4.4.2 Penalizing the Fitness of an Individual with Large Size
77(1)
4.4.3 Designing Genetic Operators
77(1)
4.5 Gene Expression Programming
78(5)
References
80(3)
5 Evolutionary Strategies
83(10)
5.1 Introduction
83(1)
5.2 Basic Algorithm
84(1)
5.3 Evolutionary Gradient Search and Gradient Evolution
85(3)
5.4 CMA Evolutionary Strategies
88(5)
References
90(3)
6 Differential Evolution
93(12)
6.1 Introduction
93(1)
6.2 DE Algorithm
94(3)
6.3 Variants of DE
97(3)
6.4 Binary DE Algorithms
100(1)
6.5 Theoretical Analysis on DE
100(5)
References
101(4)
7 Estimation of Distribution Algorithms
105(16)
7.1 Introduction
105(2)
7.2 EDA Flowchart
107(1)
7.3 Population-Based Incremental Learning
108(2)
7.4 Compact Genetic Algorithms
110(2)
7.5 Bayesian Optimization Algorithm
112(1)
7.6 Concergence Properties
112(1)
7.7 Other EDAs
113(8)
7.7.1 Probabilistic Model Building GP
115(1)
References
116(5)
8 Topics in Evolutinary Algorithms
121(32)
8.1 Convergence of Evolutinary Algorithms
121(4)
8.1.1 Schema Theorem and Building-Block Hypothesis
121(2)
8.1.2 Finite and Infinite Population Models
123(2)
8.2 Random Problems and Deceptive Functions
125(2)
8.3 Parallel Evolutionary Algorithms
127(9)
8.3.1 Master--Slave Model
129(1)
8.3.2 Island Model
130(2)
8.3.3 Cellular EAs
132(1)
8.3.4 Cooperative Coevolution
133(1)
8.3.5 Cloud Computing
134(1)
8.3.6 GPU Computing
135(1)
8.4 Coevolution
136(3)
8.4.1 Coevolutionary Approaches
137(1)
8.4.2 Coevolutionary Approach for Minimax Optimization
138(1)
8.5 Interactive Evolutionary Computation
139(1)
8.6 Fitness Approximation
139(2)
8.7 Other Heredity-Based Algorithms
141(1)
8.8 Application: Optimizating Neural Networks
142(11)
References
146(7)
9 Particle Swarm Optimization
153(22)
9.1 Introduction
153(1)
9.2 Basic PSO Algorithms
154(5)
9.2.1 Bare-Bones PSO
156(1)
9.2.2 PSO Variants Using Gaussian or Cauchy Distribution
157(1)
9.2.3 Stability Analysis of PSO
157(2)
9.3 PSO Variants Using Different Neighborhood Topologies
159(1)
9.4 Other PSO Variants
160(4)
9.5 PSO and EAs: Hybridization
164(1)
9.6 Discrete PSO
165(1)
9.7 Multi-swarm PSOs
166(9)
References
169(6)
10 Artificial Immune Systems
175(16)
10.1 Introduction
175(2)
10.2 Immunological Theories
177(3)
10.3 Immune Algorithms
180(11)
10.3.1 Clonal Selection Algorithm
180(4)
10.3.2 Artificial Immune Network
184(1)
10.3.3 Negative Selection Algorithm
185(1)
10.3.4 Dendritic Cell Algorithm
186(1)
References
187(4)
11 Ant Colony Optimization
191(10)
11.1 Introduction
191(1)
11.2 Ant-Colony Optimization
192(9)
11.2.1 Basic ACO Algorithm
194(1)
11.2.2 ACO for Continuous Optimization
195(3)
References
198(3)
12 Bee Metaheuristics
201(16)
12.1 Introduction
201(2)
12.2 Artificial Bee Colony Algorithm
203(6)
12.2.1 Algorithm Flowchart
203(4)
12.2.2 Modifications on ABC Algorithm
207(1)
12.2.3 Discrete ABC Algorithms
208(1)
12.3 Marriage in Honeybees Optimization
209(1)
12.4 Bee Colony Optimization
210(1)
12.5 Other Bee Algorithms
211(6)
12.5.1 Wasp Swarm Optimization
212(1)
References
213(4)
13 Bacterial Foraging Algorithm
217(10)
13.1 Introduction
217(2)
13.2 Bacterial Foraging Algorithm
219(3)
13.3 Algorithms Inspired by Molds, Algae, and Tumor Cells
222(5)
References
224(3)
14 Harmony Search
227(10)
14.1 Introduction
227(1)
14.2 Harmony Search Algorithm
228(2)
14.3 Variants of Harmony Search
230(3)
14.4 Melody Search
233(4)
References
234(3)
15 Swarm Intelligence
237(28)
15.1 Glowworm-Based Optimization
237(3)
15.1.1 Glowworm Swarm Optimization
238(1)
15.1.2 Firefly Algorithm
239(1)
15.2 Group Search Optimization
240(1)
15.3 Shuffled Frog Leaping
241(1)
15.4 Collective Animal Search
242(1)
15.5 Cuckoo Search
243(3)
15.6 Bat Algorithm
246(1)
15.7 Swarm Intelligence Inspired by Animal Behaviors
247(8)
15.7.1 Social Spider Optimization
247(2)
15.7.2 Fish Swarm Optimization
249(1)
15.7.3 Krill Herd Algorithm
250(1)
15.7.4 Cockroach-Based Optimization
251(1)
15.7.5 Seven-Spot Ladybird Optimization
252(1)
15.7.6 Monkey-Inspired Optimization
252(1)
15.7.7 Migrating-Based Algorithms
253(1)
15.7.8 Other Methods
254(1)
15.8 Plant-Based Metaheuristics
255(2)
15.9 Other Swarm Intelligence-Based Metaheuristics
257(8)
References
259(6)
16 Biomolecular Computing
265(18)
16.1 Introduction
265(3)
16.1.1 Biochemical Networks
267(1)
16.2 DNA Computing
268(3)
16.2.1 DNA Data Embedding
271(1)
16.3 Membrane Computing
271(12)
16.3.1 Cell-Like P System
272(1)
16.3.2 Computing by P System
273(2)
16.3.3 Other P Systems
275(2)
16.3.4 Membrane-Based Optimization
277(1)
References
278(5)
17 Quantum Computing
283(12)
17.1 Introduction
283(1)
17.2 Fundamentals
284(3)
17.2.1 Graver's Search Algorithm
286(1)
17.3 Hybrid Methods
287(8)
17.3.1 Quantum-Inspired EAs
287(3)
17.3.2 Other Quantum-Inspired Hybrid Algorithms
290(1)
References
291(4)
18 Metaheuristics Based on Sciences
295(20)
18.1 Search Based on Newton's Laws
295(2)
18.2 Search Based on Electromagnetic Laws
297(1)
18.3 Search Based on Thermal-Energy Principles
298(1)
18.4 Search Based on Natural Phenomena
299(4)
18.4.1 Search Based on Water Flows
299(2)
18.4.2 Search Based on Cosmology
301(1)
18.4.3 Black Hole-Based Optimization
302(1)
18.5 Sorting
303(1)
18.6 Algorithmic Chemistries
304(2)
18.6.1 Chemical Reaction Optimization
304(2)
18.7 Biogeography-Based Optimization
306(3)
18.8 Methods Based on Mathematical Concepts
309(6)
18.8.1 Opposition-Based Learning
310(1)
References
311(4)
19 Memetic Algorithms
315(12)
19.1 Introduction
315(1)
19.2 Cultural Algorithms
316(2)
19.3 Memetic Algorithms
318(3)
19.3.1 Simplex-based Memetic Algorithms
320(1)
19.4 Application: Searching Low Autocorrelation Sequences
321(6)
References
324(3)
20 Tabu Search and Scatter Search
327(10)
20.1 Tabu Search
327(4)
20.1.1 Iterative Tabu Search
330(1)
20.2 Scatter Search
331(2)
20.3 Path Relinking
333(4)
References
335(2)
21 Search Based on Human Behaviors
337(10)
21.1 Seeker Optimization Algorithm
337(1)
21.2 Teaching-Learning-Based Optimization
338(2)
21.3 Imperialist Competitive Algorithm
340(2)
21.4 Several Metaheuristics Inspired by Human Behaviors
342(5)
References
345(2)
22 Dynamic, Multimodal, and Constrained Optimizations
347(24)
22.1 Dynamic Optimization
347(3)
22.1.1 Memory Scheme
348(1)
22.1.2 Diversity Maintaining or Reinforcing
348(1)
22.1.3 Multiple Population Scheme
349(1)
22.2 Multimodal Optimization
350(9)
22.2.1 Crowding and Restricted Tournament Selection
351(2)
22.2.2 Fitness Sharing
353(1)
22.2.3 Speciation
354(2)
22.2.4 Clearing, Local Selection, and Demes
356(1)
22.2.5 Other Methods
357(2)
22.2.6 Metrics for Multimodal Optimization
359(1)
22.3 Constrained Optimization
359(12)
22.3.1 Penalty Function Method
360(3)
22.3.2 Using Multiobjective Optimization Techniques
363(2)
References
365(6)
23 Multiobjective Optimization
371(42)
23.1 Introduction
371(2)
23.2 Multiobjective Evolutionary Algorithms
373(13)
23.2.1 Nondominated Sorting Genetic Algorithm II
374(3)
23.2.2 Strength Pareto Evolutionary Algorithm 2
377(1)
23.2.3 Pareto Archived Evolution Strategy (PAES)
378(1)
23.2.4 Pareto Envelope-Based Selection Algorithm
379(1)
23.2.5 MOEA Based on Decomposition (MOEA/D)
380(1)
23.2.6 Several MOEAs
381(3)
23.2.7 Nondominated Sorting
384(1)
23.2.8 Multiobjective Optimization Based on Differential Evolution
385(1)
23.3 Performance Metrics
386(3)
23.4 Many-Objective Optimization
389(5)
23.4.1 Challenges in Many-Objective Optimization
389(2)
23.4.2 Pareto-Based Algorithms
391(2)
23.4.3 Decomposition-Based Algorithms
393(1)
23.5 Multiobjective Immune Algorithms
394(1)
23.6 Multiobjective PSO
395(3)
23.7 Multiobjective EDAs
398(1)
23.8 Tabu/Scatter Search Based Multiobjective Optimization
399(1)
23.9 Other Methods
400(2)
23.10 Revolutionary MOEAs
402(11)
References
403(10)
Appendix A Benchmarks 413(18)
Index 431
Ke-Lin Du, PhD, is Affiliate Associate Professor at Concordia University, Montreal, Quebec, Canada, and Founder and CEO of Xonlink Inc, Ningbo, China. M.N.S. Swamy, PhD, is Research Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Quebec, Canada.