Muutke küpsiste eelistusi

E-raamat: Optimization in Engineering Sciences: Metaheuristic, Stochastic Methods and Decision Support

, , , ,
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Oct-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118648773
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 198,77 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Optimization in Engineering Sciences: Metaheuristic, Stochastic Methods and Decision SupportSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Oct-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118648773
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.

List Of Figures ix
List Of Tables xiii
List Of Algorithms xv
List Of Acronyms xvii
Preface xix
Acknowledgements xxi
Chapter 1 Metaheuristics - Local Methods 1(52)
1.1 Overview
1(5)
1.2 Monte Carlo principle
6(6)
1.3 Hill climbing
12(8)
1.4 Taboo search
20(19)
1.4.1 Principle
20(1)
1.4.2 Greedy descent algorithm
20(3)
1.4.3 Taboo search method
23(2)
1.4.4 Taboo list
25(1)
1.4.5 Taboo search algorithm
26(4)
1.4.6 Intensification and diversification
30(1)
1.4.7 Application examples
31(8)
1.5 Simulated annealing
39(7)
1.5.1 Principle of thermal annealing
39(2)
1.5.2 Kirkpatrick's model of thermal annealing
41(2)
1.5.3 Simulated annealing algorithm
43(3)
1.6 Tunneling
46(5)
1.6.1 Tunneling principle
46(2)
1.6.2 Types of tunneling
48(1)
1.6.3 Tunneling algorithm
49(2)
1.7 GRASP methods
51(2)
Chapter 2 Metaheuristics - Global Methods 53(166)
2.1 Principle of evolutionary metaheuristics
53(2)
2.2 Genetic algorithms
55(45)
2.2.1 Biology breviary
55(2)
2.2.2 Features of genetic algorithms
57(16)
2.2.3 General structure of a GA
73(4)
2.2.4 On the convergence of GA
77(7)
2.2.5 How to implement a genetic algorithm
84(16)
2.3 Hill climbing by evolutionary strategies
100(7)
2.3.1 Climbing by the steepest ascent
101(3)
2.3.2 Climbing by the next ascent
104(2)
2.3.3 Hill climbing by group of alpinists
106(1)
2.4 Optimization by ant colonies
107(25)
2.4.1 Ant colonies
107(3)
2.4.2 Basic optimization algorithm by ant colonies
110(8)
2.4.3 Pheromone trail update
118(4)
2.4.4 Systemic ant colony algorithm
122(6)
2.4.5 Traveling salesman example
128(4)
2.5 Particle swarm optimization
132(75)
2.5.1 Basic metaheuristic
132(9)
2.5.2 Standard PSO algorithm
141(5)
2.5.3 Adaptive PSO algorithm with evolutionary strategy
146(17)
2.5.4 Fireflies algorithm
163(10)
2.5.5 Bats algorithm
173(9)
2.5.6 Bees algorithm
182(12)
2.5.7 Multivariable prediction by PSO
194(13)
2.6 Optimization by harmony search
207(12)
2.6.1 Musical composition and optimization
207(1)
2.6.2 Harmony search model
208(4)
2.6.3 Standard harmony search algorithm
212(3)
2.6.4 Application example
215(4)
Chapter 3 Stochastic Optimization 219(34)
3.1 Introduction
219(2)
3.2 Stochastic optimization problem
221(1)
3.3 Computing the repartition function of a random variable
222(8)
3.4 Statistical criteria for optimality
230(10)
3.4.1 Case of totally admissible solutions
231(3)
3.4.2 Case of partially admissible solutions
234(6)
3.5 Examples
240(5)
3.6 Stochastic optimization through games theory
245(8)
3.6.1 Principle
245(2)
3.6.2 Wald strategy (maximin)
247(1)
3.6.3 Hurwicz strategy
248(1)
3.6.4 Laplace strategy
249(1)
3.6.5 Bayes-Laplace strategy
249(1)
3.6.6 Savage strategy
250(1)
3.6.7 Example
251(2)
Chapter 4 Multi-Criteria Optimization 253(56)
4.1 Introduction
253(2)
4.2 Introductory examples
255(2)
4.2.1 Choosing the first job
255(1)
4.2.2 Selecting an IT tool
256(1)
4.2.3 Setting the production rate of a continuous process plant
256(1)
4.3 Multi-criteria optimization problems
257(8)
4.3.1 Two subclasses of problems
257(5)
4.3.2 Dominance and Pareto optimality
262(3)
4.4 Model solving methods
265(27)
4.4.1 Classifications
265(1)
4.4.2 Substitution-based methods
266(4)
4.4.3 Aggregation-based methods
270(12)
4.4.4 Other methods
282(10)
4.5 Two objective functions optimization for advanced control systems
292(15)
4.5.1 Aggregating identification with the design of a dynamical control system
292(10)
4.5.2 Aggregating decision model identification with the supervision
302(5)
4.6 Notes and comments
307(2)
Chapter 5 Methods And Tools For Model-Based Decision-Making 309(42)
5.1 Introduction
309(1)
5.2 Introductory examples
310(3)
5.2.1 Choosing a job: probabilistic case
310(1)
5.2.2 Starting a business
311(1)
5.2.3 Selecting an IT engineer
311(2)
5.3 Decisions and decision activities. Basic concepts
313(3)
5.3.1 Definition
313(1)
5.3.2 Approaches
314(2)
5.4 Decision analysis
316(31)
5.4.1 Preliminary analysis: preparing the choice
317(13)
5.4.2 Making a choice: structuring and solving decision problems
330(17)
5.5 Notes and comments
347(1)
5.6 Other remarks/comments
347(4)
Chapter 6 Decision-Making - Case Study Simulation 351(18)
6.1 Decision problem in uncertain environment
351(1)
6.2 Problem statement
352(1)
6.3 Simulation principle
353(4)
6.4 Case studies
357(12)
6.4.1 Stock management
358(4)
6.4.2 Competitive tender
362(3)
6.4.3 Queuing process or ATM
365(4)
Appendix 1 369(8)
Appendix 2 377(16)
Bibliography 393(20)
Index 413
Dan Stefanoiu is Professor in the fields of Signal Processing and System Identification at Politehnica University of Bucharest. In 2002 he was elected as a member of the American Romanian Academy of Arts and Sciences (ARA).

Pierre Borne is Professor at École Centrale de Lille, France. He has received honorary degrees from the University of Moscow, Russia, the Politehnica University of Bucharest, Romania, and the University of Waterloo, Canada. He is a Fellow of the IEEE.

Dumitru Popescu is Professor at Politehnica University of Bucharest, Romania, in the fields of Advanced Control and Systems Optimization and also Associate Professor at major universities in France and Italy. He is Director of the Research Center in Automatics, Process Control and Computers (APCC) affiliated to Politehnica University of Bucharest, a member of the IFAC Technical Committees for the Control of Bioprocesses and Chemical Processes and a corresponding member of the Romanian Academy of Technical Sciences.

Florin Gh. Filip is a researcher and Professor in the fields of Optimization and Control of Large-scale Systems, applied IT including decision support systems. He was elected as a member of the Romanian Academy (National Academy of Sciences of Romania) in 1991 and President of the "Information Science and Technology" section of the Academy in 2011. He was the Vice President of the Romanian Academy from 2001 to 2010 and the chair of the IFAC TC 5.4 "Large-scale complex systems" from 2002 to 2008.

Abdelkader El Kamel is Professor in the fields of Advanced Control, System Optimization and Decision-Making at École Centrale de Lille in France. He is also a regular Visiting Professor, mainly in China, Chile and at major engineering and business schools in Tunisia.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811186487732e.html
Märksõnad: