Nature-inspired computing and optimization : theory and applications
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. Applicatio...
Saved in:
| Other Authors | , , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Cham, Switzerland :
Springer,
2017.
|
| Series | Modeling and optimization in science and technologies ;
v. 10. |
| Subjects | |
| Online Access | Full text |
| ISBN | 9783319509204 9783319509198 |
| ISSN | 2196-7326 ; |
| Physical Description | 1 online resource (xxi, 494 pages) : illustrations (some color) |
Cover
| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | 99876 | ||
| 003 | CZ-ZlUTB | ||
| 005 | 20251008112002.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 170310s2017 sz a ob 000 0 eng d | ||
| 040 | |a N$T |b eng |e rda |e pn |c N$T |d IDEBK |d GW5XE |d N$T |d EBLCP |d OCLCF |d YDX |d UAB |d NJR |d IOG |d AZU |d UPM |d MERER |d ESU |d OCLCQ |d JBG |d IAD |d ICW |d ICN |d VT2 |d OTZ |d OCLCQ |d U3W |d CAUOI |d KSU |d WYU |d UKMGB |d OCLCQ |d UKAHL |d OCLCQ |d ERF |d UKBTH |d LEATE |d OCLCQ |d SRU | ||
| 020 | |a 9783319509204 |q (electronic bk.) | ||
| 020 | |z 9783319509198 |q (print) | ||
| 024 | 7 | |a 10.1007/978-3-319-50920-4 |2 doi | |
| 035 | |a (OCoLC)974947287 |z (OCoLC)984867447 |z (OCoLC)999587522 |z (OCoLC)1005816949 |z (OCoLC)1011793150 |z (OCoLC)1048172437 |z (OCoLC)1066630754 |z (OCoLC)1066679442 |z (OCoLC)1086540212 |z (OCoLC)1112564491 |z (OCoLC)1112847650 |z (OCoLC)1112964986 |z (OCoLC)1116058263 |z (OCoLC)1122811996 |z (OCoLC)1127212021 | ||
| 245 | 0 | 0 | |a Nature-inspired computing and optimization : |b theory and applications / |c Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu, editors. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2017. | |
| 300 | |a 1 online resource (xxi, 494 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 490 | 1 | |a Modeling and optimization in science and technologies, |x 2196-7326 ; |v volume 10 | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Preface; Contents; Contributors; Members of Review Board; The Nature of Nature: Why Nature-Inspired Algorithms Work; 1 Introduction: How Nature Works; 2 The Nature of Nature; 2.1 Fitness Landscape; 2.2 Graphs and Phase Changes; 3 Nature-Inspired Algorithms; 3.1 Genetic Algorithm; 3.2 Ant Colony Optimization; 3.3 Simulated Annealing; 3.4 Convergence; 4 Dual-Phase Evolution; 4.1 Theory; 4.2 GA; 4.3 Ant Colony Optimization; 4.4 Simulated Annealing; 5 Evolutionary Dynamics; 5.1 Markov Chain Models; 5.2 The Replicator Equation; 6 Generalized Local Search Machines; 6.1 The Model; 6.2 SA; 6.3 GA. | |
| 505 | 8 | |a 6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems. | |
| 505 | 8 | |a 2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments. | |
| 505 | 8 | |a 5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions. | |
| 505 | 8 | |a 2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin -- Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References. | |
| 505 | 8 | |a Hybridization of the Flower Pollination Algorithm -- A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a 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 algorithms, 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. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Natural computation. | |
| 650 | 0 | |a Mathematical optimization. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Patnaik, Srikanta, |e editor. | |
| 700 | 1 | |a Yang, Xin-She, |e editor. | |
| 700 | 1 | |a Nakamatsu, Kazumi, |e editor. | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319509198 |
| 830 | 0 | |a Modeling and optimization in science and technologies ; |v v. 10. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-50920-4 |
| 992 | |c NTK-SpringerENG | ||
| 999 | |c 99876 |d 99876 | ||
| 993 | |x NEPOSILAT |y EIZ | ||