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Nature-Inspired Computing and Optimization: Theory and Applications 1st ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 494 pages, kõrgus x laius: 235x155 mm, kaal: 8926 g, 43 Illustrations, color; 148 Illustrations, black and white; XXI, 494 p. 191 illus., 43 illus. in color., 1 Hardback
  • Sari: Modeling and Optimization in Science and Technologies 10
  • Ilmumisaeg: 16-Mar-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319509195
  • ISBN-13: 9783319509198
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Nature-Inspired Computing and Optimization: Theory and Applications 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 494 pages, kõrgus x laius: 235x155 mm, kaal: 8926 g, 43 Illustrations, color; 148 Illustrations, black and white; XXI, 494 p. 191 illus., 43 illus. in color., 1 Hardback
  • Sari: Modeling and Optimization in Science and Technologies 10
  • Ilmumisaeg: 16-Mar-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319509195
  • ISBN-13: 9783319509198
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319509198.html
Märksõnad:
The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorit

hms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.

From the content: The Nature of Nature: Why Nature Inspired Algorithms Work.- Improved Bat Algorithm in Noise-Free and Noisy Environments.- Multi-objective Ant Colony Optimisation in Wireless Sensor Networks.le
The Nature of Nature: Why Nature-Inspired Algorithms Work
1(28)
David Green
Aldeida Aleti
Julian Garcia
1 Introduction: How Nature Works
1(1)
2 The Nature of Nature
2(4)
2.1 Fitness Landscape
3(1)
2.2 Graphs and Phase Changes
4(2)
3 Nature-Inspired Algorithms
6(3)
3.1 Genetic Algorithm
6(1)
3.2 Ant Colony Optimization
7(1)
3.3 Simulated Annealing
7(1)
3.4 Convergence
8(1)
4 Dual-Phase Evolution
9(4)
4.1 Theory
9(1)
4.2 GA
9(2)
4.3 Ant Colony Optimization
11(1)
4.4 Simulated Annealing
12(1)
5 Evolutionary Dynamics
13(5)
5.1 Markov Chain Models
13(2)
5.2 The Replicator Equation
15(3)
6 Generalized Local Search Machines
18(4)
6.1 The Model
18(1)
6.2 SA
19(1)
6.3 GA
20(1)
6.4 ACO
21(1)
6.5 Discussion
21(1)
7 Conclusion
22(7)
References
24(5)
Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments
29(22)
Momin Jamil
Hans-Jurgen Zepernick
Xin-She Yang
1 Introduction
30(1)
2 Improved Bat Algorithm
31(3)
3 IBA for Multimodal Problems
34(7)
3.1 Parameter Settings
34(1)
3.2 Test Functions
35(1)
3.3 Numerical Results
35(6)
4 Performance Comparison of IBA with Other Algorithms
41(1)
5 IBA Performance in AWGN
42(5)
5.1 Numerical Results
44(3)
6 Conclusions
47(4)
References
47(4)
Multi-objective Ant Colony Optimisation in Wireless Sensor Networks
51(28)
Ansgar Kellner
1 Introduction
51(1)
2 Multi-objective Combinatorial Optimisation Problems
52(8)
2.1 Combinatorial Optimisation Problems
52(1)
2.2 Multi-objective Combinatorial Optimisation Problems
53(1)
2.3 Pareto Optimality
54(1)
2.4 Decision-Making
55(4)
2.5 Solving Combinatorial Optimisation Problems
59(1)
3 Multi-objective Ant Colony Optimisation
60(12)
3.1 Origins
60(6)
3.2 Multi-objective Ant Colony Optimisation
66(6)
4 Applications of MOACO Algorithms in WSNs
72(2)
5 Conclusion
74(5)
References
74(5)
Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence
79(16)
Iztok Fister Jr.
Iztok Fister
1 Introduction
79(2)
2 Artificial Sports Trainer
81(1)
3 Generating the Training Plans
82(9)
3.1 Preprocessing
84(3)
3.2 Optimization Process
87(4)
4 Experiments
91(2)
5 Conclusion with Future Ideas
93(2)
References
93(2)
Limiting Distribution and Mixing Time for Genetic Algorithms
95(28)
S. Alwadani
F. Mendivil
R. Shonkwiler
1 Introduction
95(1)
2 Preliminaries
96(5)
2.1 Random Search and Markov Chains
98(1)
2.2 Boltzmann Distribution and Simulated Annealing
99(2)
3 Expected Hitting Time as a Means of Comparison
101(4)
3.1 "No Free Lunch" Considerations
104(1)
4 The Holland Genetic Algorithm
105(4)
5 A Simple Genetic Algorithm
109(6)
6 Shuffle-Bit GA
115(5)
6.1 Results
117(1)
6.2 Estimate of Expected Hitting Time
118(2)
7 Discussion and Future Work
120(3)
References
121(2)
Permutation Problems, Genetic Algorithms, and Dynamic Representations
123(28)
James Alexander Hughes
Sheridan Houghten
Daniel Ashlock
1 Introduction
123(2)
2 Problem Descriptions
125(2)
2.1 Bin Packing Problem
125(1)
2.2 Graph Colouring Problem
125(1)
2.3 Travelling Salesman Problem
126(1)
3 Previous Work on Small Travelling Salesman Problem Instances
127(1)
4 Algorithms
128(7)
4.1 2-Opt
128(1)
4.2 Lin--Kernighan
129(1)
4.3 Genetic Algorithm Variations
129(5)
4.4 Representation
134(1)
5 Experimental Design
135(3)
5.1 Bin Packing Problem
135(1)
5.2 Graph Colouring Problem
136(1)
5.3 Travelling Salesman Problem
137(1)
6 Results and Discussion
138(9)
6.1 Bin Packing Problem
138(4)
6.2 Graph Colouring Problem
142(2)
6.3 Travelling Salesman Problem
144(3)
7 Conclusions
147(4)
References
148(3)
Hybridization of the Flower Pollination Algorithm---A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults
151(34)
Cristina Bianca Pop
Viorica Rozina Chifu
Ioan Salomie
Dalma Szonja Racz
Razvan Mircea Bonta
1 Introduction
152(1)
2 Background
153(3)
2.1 Optimization Problems
153(1)
2.2 Meta-Heuristic Algorithms
154(2)
3 Literature Review
156(2)
4 Problem Definition
158(4)
4.1 Search Space and Solution Representation
158(1)
4.2 Fitness Function
159(2)
4.3 Constraints
161(1)
5 Hybridizing the Flower Pollination Algorithm for Generating Personalized Menu Recommendations
162(6)
5.1 Hybrid Flower Pollination-Based Model
162(2)
5.2 Flower Pollination-Based Algorithms for Generating Personalized Menu Recommendations
164(2)
5.3 The Iterative Stage of the Hybrid Flower Pollination-Based Algorithm for Generating Healthy Menu Recommendations
166(2)
6 Performance Evaluation
168(13)
6.1 Experimental Prototype
168(3)
6.2 Test Scenarios
171(1)
6.3 Setting the Optimal Values of the Algorithms' Adjustable Parameters
172(7)
6.4 Comparison Between the Classical and Hybrid Flower Pollination-Based Algorithms
179(2)
7 Conclusions
181(4)
References
182(3)
Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System
185(32)
Gopi Ram
Durbadal Mandal
S.P. Ghoshal
Rajib Kar
1 Introduction
186(1)
2 Problem Formulation
187(2)
3 Flower Pollination Algorithm [ 55]
189(4)
3.1 Global Pollination
190(1)
3.2 Local Pollination
190(1)
3.3 Pseudo-code for FPA
191(2)
4 Simulation Results
193(18)
4.1 Optimization of Hyper-Beam by Using FPA
194(2)
4.2 Comparisons of Accuracies Based on t test
196(15)
5 Convergence Characteristics of Different Algorithms
211(1)
6 Conclusion
212(1)
7 Future Research Topics
212(5)
References
212(5)
Multi-Agent Optimization of Resource-Constrained Project Scheduling Problem Using Nature-Inspired Computing
217(30)
Pragyan Nanda
Sritam Patnaik
Srikanta Patnaik
1 Introduction
217(3)
1.1 Multi-agent System
218(1)
1.2 Scheduling
219(1)
1.3 Nature-Inspired Computing
220(1)
2 Resource-Constrained Project Scheduling Problem
220(2)
3 Various Nature-Inspired Computation Techniques for RCPSP
222(18)
3.1 Particle Swarm Optimization (PSO)
223(1)
3.2 Particle Swarm Optimization (PSO) for RCPSP
224(3)
3.3 Ant Colony Optimization (ACO)
227(2)
3.4 Ant Colony Optimization (ACO) for RCPSP
229(1)
3.5 Shuffled Frog-Leaping Algorithm (SFLA)
230(2)
3.6 Shuffled Frog-Leaping Algorithm (SFLA) for RCPSP
232(3)
3.7 Multi-objective Invasive Weed Optimization
235(1)
3.8 Multi-objective Invasive Weed Optimization for MRCPSP
235(1)
3.9 Discrete Flower Pollination
236(1)
3.10 Discrete Flower Pollination for RCPSP
237(1)
3.11 Discrete Cuckoo Search
237(1)
3.12 Discrete Cuckoo Search for RCPSP
238(1)
3.13 Multi-agent Optimization Algorithm (MAOA)
238(2)
4 Proposed Approach
240(2)
4.1 RCPSP for Retail Industry
240(1)
4.2 Cooperative Hunting Behaviour of Lion Pride
240(2)
5 A Lion Pride-Inspired Multi-Agent System-Based Approach for RCPSP
242(2)
6 Conclusion
244(3)
References
244(3)
Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology
247(30)
Changhee Han
Kenji Tsuge
Hitoshi Iba
1 Introduction
248(1)
2 Learning Classifier Systems: Creating Rules that Describe Systems
249(2)
2.1 Basic Components
250(1)
2.2 Michigan- and Pittsburgh-style LCS
250(1)
3 Examples of LCS
251(4)
3.1 Minimal Classifier Systems
251(1)
3.2 Zeroth-level Classifier Systems
252(2)
3.3 Extended Classifier Systems
254(1)
4 Synthetic Biology: Designing Biological Systems
255(4)
4.1 The Synthetic Biology Design Cycle
255(1)
4.2 Basic Biological Parts
256(1)
4.3 DNA Construction
257(1)
4.4 Future Applications
257(2)
5 Gene Expression Analysis with LCS
259(2)
6 Optimization of Artificial Operon Structure
261(1)
7 Optimization of Artificial Operon Construction by Machine Learning
262(10)
7.1 Introduction
262(1)
7.2 Artificial Operon Model
262(1)
7.3 Experimental Framework
263(3)
7.4 Results
266(4)
7.5 Conclusion
270(2)
8 Summary
272(5)
References
272(5)
Ant Colony Optimization for Semantic Searching of Distributed Dynamic Multiclass Resources
277(28)
Kamil Krynicki
Javier Jaen
1 Introduction
277(2)
2 P2p Search Strategies
279(5)
3 Nature-Inspired Ant Colony Optimization
284(3)
4 Nature-Inspired Strategies in Dynamic Networks
287(5)
4.1 Network Dynamism Inefficiency
288(1)
4.2 Solution Framework
289(1)
4.3 Experimental Evaluation
290(2)
5 Nature-Inspired Strategies of Semantic Nature
292(9)
5.1 Semantic Query Inefficiency
292(1)
5.2 Solution Framework
293(5)
5.3 Experimental Evaluation
298(3)
6 Conclusions and Future Developments
301(4)
References
302(3)
Adaptive Virtual Topology Control Based on Attractor Selection
305(24)
Yuki Koizumi
Shin'ichi Arakawa
Masayuki Murata
1 Introduction
306(2)
2 Related Work
308(1)
3 Attractor Selection
308(4)
3.1 Concept of Attractor Selection
309(1)
3.2 Cell Model
309(1)
3.3 Mathematical Model of Attractor Selection
310(2)
4 Virtual Topology Control Based on Attractor Selection
312(6)
4.1 Virtual Topology Control
312(1)
4.2 Overview of Virtual Topology Control Based on Attractor Selection
312(2)
4.3 Dynamics of Virtual Topology Control
314(2)
4.4 Attractor Structure
316(1)
4.5 Dynamic Reconfiguration of Attractor Structure
317(1)
5 Performance Evaluation
318(8)
5.1 Simulation Conditions
318(3)
5.2 Dynamics of Virtual Topology Control Based on Attractor Selection
321(2)
5.3 Adaptability to Node Failures
323(1)
5.4 Effects of Noise Strength
324(1)
5.5 Effects of Activity
324(1)
5.6 Effects of Reconfiguration Methods of Attractor Structure
325(1)
6 Conclusion
326(3)
References
327(2)
CBO-Based TDR Approach for Wiring Network Diagnosis
329(20)
Hamza Boudjefdjouf
Francesco de Paulis
Houssem Bouchekara
Antonio Orlandi
Mostafa K. Smail
1 Introduction
330(2)
2 The Proposed TDR-CBO-Based Approach
332(7)
2.1 Problem Formulation
332(1)
2.2 The Forward Model
333(4)
2.3 Colliding Bodies Optimization (CBO)
337(2)
3 Applications and Results
339(8)
3.1 The Y-Shaped Wiring Network
340(4)
3.2 The YY-shaped Wiring Network
344(3)
4 Conclusion
347(2)
References
348(1)
Morphological Filters: An Inspiration from Natural Geometrical Erosion and Dilation
349(32)
Mahdi Khosravy
Neeraj Gupta
Ninoslav Marina
Ishwar K. Sethi
Mohammad Reza Asharif
1 Natural Geometrical Inspired Operators
350(1)
2 Mathematical Morphology
351(2)
2.1 Morphological Filters
352(1)
3 Morphological Operators and Set Theory
353(16)
3.1 Sets and Corresponding Operators
354(2)
3.2 Basic Properties for Morphological Operators
356(1)
3.3 Set Dilation and Erosion
357(2)
3.4 A Geometrical Interpretation of Dilation and Erosion Process
359(1)
3.5 Direct Effect of Edges and Borders on the Erosion and Dilation
360(3)
3.6 Closing and Opening
363(4)
3.7 A Historical Review to Definitions and Notations
367(2)
4 Practical Interpretation of Binary Opening and Closing
369(1)
5 Morphological Operators in Grayscale Domain
370(6)
5.1 Basic Morphological Operators in Multivalued Function Domain
370(4)
5.2 Dilation and Erosion of Multivalued Functions
374(1)
5.3 Two Forms of Presentation for Dilation and Erosion Formula
375(1)
6 Opening and Closing of Multivalued Functions
376(1)
7 Interpretation and Intuitive Understanding of Morphological Filters in Multivalued Function Domain
377(2)
8 Conclusion
379(2)
References
379(2)
Brain Action Inspired Morphological Image Enhancement
381(28)
Mahdi Khosravy
Neeraj Gupta
Ninoslav Marina
Ishwar K. Sethi
Mohammad Reza Asharif
1 Introduction
382(1)
2 Human Visual Perception
383(1)
3 Visual Illusions
384(2)
4 Visual Illusions
386(7)
4.1 Rotating Snakes
388(5)
5 Mach Bands Illusion
393(1)
6 Image Enhancement Inspiration from Human Visual Illusion
393(1)
7 Morphological Image Enhancement Based on Visual Illusion
394(4)
8 Results and Discussion
398(5)
9 Summary
403(6)
References
406(3)
Path Generation for Software Testing: A Hybrid Approach Using Cuckoo Search and Bat Algorithm
409(16)
Praveen Ranjan Srivastava
1 Introduction
409(1)
2 Related Work
410(1)
3 Motivational Algorithm
411(3)
3.1 Cuckoo Search Algorithm
411(1)
3.2 Bat Algorithm [ 12]
412(2)
4 Proposed Algorithm
414(2)
5 Path Sequence Generation and Prioritization
416(5)
6 Analysis of Proposed Algorithm
421(1)
7 Conclusions and Future Scope
422(3)
References
423(2)
An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem
425(24)
Ahmed Fouad Ali
1 Introduction
425(1)
2 Related Work
426(1)
3 Economic Dispatch Problem
427(2)
3.1 Problem Constraints
427(1)
3.2 Penalty Function
428(1)
4 Social Behavior and Foraging of Spider Monkeys
429(7)
4.1 Fission-Fusion Social Behavior
429(1)
4.2 Social Organization and Behavior
429(1)
4.3 Communication of Spider Monkeys
430(1)
4.4 Characteristic of Spider Monkeys
430(1)
4.5 The Standard Spider Monkey Optimization Algorithm
430(4)
4.6 Spider Monkey Optimization Algorithm
434(2)
5 Multidirectional Search Algorithm
436(3)
6 The Proposed MDSMO Algorithm
439(1)
7 Numerical Experiments
439(4)
7.1 Parameter Setting
439(1)
7.2 Six-Generator Test System with System Losses
440(1)
7.3 The General Performance of the Proposed MDSMO with Economic Dispatch Problem
441(1)
7.4 MDSMO and Other Algorithms
441(2)
8 Conclusion and Future Work
443(6)
References
446(3)
Chance-Constrained Fuzzy Goal Programming with Penalty Functions for Academic Resource Planning in University Management Using Genetic Algorithm
449(26)
Bijay Baran Pal
R. Sophia Porchelvi
Animesh Biswas
1 Introduction
449(4)
2 FGP Problem Formulation
453(3)
2.1 Membership Function Characterization
453(1)
2.2 Deterministic Equivalents of Chance Constraints
454(2)
3 Formulation of Priority Based FGP Model
456(2)
3.1 Euclidean Distance Function for Priority Structure Selection
457(1)
4 FGP Model with Penalty Functions
458(3)
4.1 Penalty Function Description
458(2)
4.2 Priority Based FGP Model with Penalty Functions
460(1)
4.3 GA Scheme for FGP Model
460(1)
5 FGP Formulation of the Problem
461(3)
5.1 Definitions of Decision Variables and Parameters
461(1)
5.2 Descriptions of Fuzzy Goals and Constraints
462(2)
6 A Case Example
464(7)
6.1 An Illustration for Performance Comparison
469(2)
7 Conclusions
471(4)
References
472(3)
Swarm Intelligence: A Review of Algorithms
475
Amrita Chakraborty
Arpan Kumar Kar
1 Introduction
476(1)
2 Research Methodology
477(1)
3 Insect-Based Algorithms
478(6)
3.1 Ant Colony Optimization Algorithm
478(2)
3.2 Bee-Inspired Algorithms
480(1)
3.3 Firefly-Based Algorithms
481(2)
3.4 Glow-Worm-Based Algorithms
483(1)
4 Animal-Based Algorithms
484(3)
4.1 Bat-Based Algorithm
484(1)
4.2 Monkey-Based Algorithm
485(1)
4.3 Lion-Based Algorithm
486(1)
4.4 Wolf-Based Algorithm
486(1)
5 Future Research Directions
487(1)
6 Conclusions
488
References
488