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Optimization in Engineering Sciences : Approximate and Metaheuristic Methods.

By: Contributor(s): Publisher: Somerset : John Wiley & Sons, Incorporated, 2014Copyright date: ©2014Edition: 1st edDescription: 1 online resource (446 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118648773
Subject(s): Genre/Form: Additional physical formats: Print version:: Optimization in Engineering Sciences : Approximate and Metaheuristic MethodsDDC classification:
  • 620.0011
LOC classification:
  • QA402.5 -- .S74 2014eb
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- List of Acronyms -- Preface -- Acknowledgments -- 1: Metaheuristics - Local Methods -- 1.1. Overview -- 1.2. Monte Carlo principle -- 1.3. Hill climbing -- 1.4. Taboo search -- 1.4.1. Principle -- 1.4.2. Greedy descent algorithm -- 1.4.3. Taboo search method -- 1.4.4. Taboo list -- 1.4.5. Taboo search algorithm -- 1.4.6. Intensification and diversification -- 1.4.7. Application examples -- 1.4.7.1. Searching the smallest value on a table -- 1.4.7.2. The problem of N queens -- 1.5. Simulated annealing -- 1.5.1. Principle of thermal annealing -- 1.5.2. Kirkpatrick's model of thermal annealing -- 1.5.3. Simulated annealing algorithm -- 1.6. Tunneling -- 1.6.1. Tunneling principle -- 1.6.2. Types of tunneling -- 1.6.2.1. Stochastic tunneling -- 1.6.2.2. Tunneling with penalties -- 1.6.3. Tunneling algorithm -- 1.7. GRASP methods -- 2: Metaheuristics - Global Methods -- 2.1. Principle of evolutionary metaheuristics -- 2.2. Genetic algorithms -- 2.2.1. Biology breviary -- 2.2.2. Features of genetic algorithms -- 2.2.2.1. Genetic operations -- 2.2.2.2. Inheritors viability -- 2.2.2.3. Selection for reproduction -- 2.2.2.3.1. Selection by fitness -- 2.2.2.3.2. Selection by σ-normalization -- 2.2.2.3.3. Selection by Boltzmann's law -- 2.2.2.3.4. Selection by ranking -- 2.2.2.3.5. Selection by tournament -- 2.2.2.3.6. Elitist selection -- 2.2.2.4. Selection for survival -- 2.2.2.4.1. Generational selection -- 2.2.2.4.2. Elitist selection -- 2.2.2.4.3. Generational elitist selection -- 2.2.3. General structure of a GA -- 2.2.4. On the convergence of GA -- 2.2.5. How to implement a genetic algorithm -- 2.3. Hill climbing by evolutionary strategies -- 2.3.1. Climbing by the steepest ascent -- 2.3.2. Climbing by the next ascent.
2.3.3. Hill climbing by group of alpinists -- 2.4. Optimization by ant colonies -- 2.4.1. Ant colonies -- 2.4.1.1. Natural ants -- 2.4.1.2. Aspects inspired from natural ants -- 2.4.1.3. Features developed for the artificial ants -- 2.4.2. Basic optimization algorithm by ant colonies -- 2.4.3. Pheromone trail update -- 2.4.3.1. Adaptive delayed update -- 2.4.3.2. On-line update -- 2.4.3.3. Update through elitist strategy -- 2.4.3.4. Update by ants ranking -- 2.4.4. Systemic ant colony algorithm -- 2.4.5. Traveling salesman example -- 2.5. Particle swarm optimization -- 2.5.1. Basic metaheuristic -- 2.5.1.1. Principle -- 2.5.1.2. Particles dynamical model -- 2.5.1.3. Selecting the informants -- 2.5.2. Standard PSO algorithm -- 2.5.3. Adaptive PSO algorithm with evolutionary strategy -- 2.5.4. Fireflies algorithm -- 2.5.4.1. Principle -- 2.5.4.2. Dynamical model of fireflies behavior -- 2.5.4.3. Standard fireflies algorithm -- 2.5.5. Bats algorithm -- 2.5.5.1. Principle -- 2.5.5.2. Dynamical model of bats behavior -- 2.5.5.3. Standard bats algorithm -- 2.5.6. Bees algorithm -- 2.5.6.1. Principle -- 2.5.6.2. Dynamical and cooperative model of bees' behavior -- 2.5.6.3. Standard bee algorithm -- 2.5.7. Multivariable prediction by PSO -- 2.6. Optimization by harmony search -- 2.6.1. Musical composition and optimization -- 2.6.2. Harmony search model -- 2.6.3. Standard harmony search algorithm -- 2.6.4. Application example -- 3: Stochastic Optimization -- 3.1. Introduction -- 3.2. Stochastic optimization problem -- 3.3. Computing the repartition function of a random variable -- 3.4. Statistical criteria for optimality -- 3.4.1. Case of totally admissible solutions -- 3.4.2. Case of partially admissible solutions -- 3.5. Examples -- 3.6. Stochastic optimization through games theory -- 3.6.1. Principle -- 3.6.2. Wald strategy (maximin).
3.6.3. Hurwicz strategy -- 3.6.4. Laplace strategy -- 3.6.5. Bayes-Laplace strategy -- 3.6.6. Savage strategy -- 3.6.7. Example -- 4: Multi-Criteria Optimization -- 4.1. Introduction -- 4.2. Introductory examples -- 4.2.1. Choosing the first job -- 4.2.2. Selecting an IT tool -- 4.2.3. Setting the production rate of a continuous process plant -- 4.3. Multi-criteria optimization problems -- 4.3.1. Two subclasses of problems -- 4.3.1.1. Multi-attribute problem subclass -- 4.3.2. Dominance and Pareto optimality -- 4.4. Model solving methods -- 4.4.1. Classifications -- 4.4.2. Substitution-based methods -- 4.4.2.1. Setting additional constraints -- 4.4.2.2. Goal programming -- 4.4.2.3. Progressive solving -- 4.4.2.3.1. Method steps (minimization case) -- 4.4.3. Aggregation-based methods -- 4.4.3.1. Definition and requirements -- 4.4.3.2. Simple weighted averaging method -- 4.4.3.3. Distance-based methods -- 4.4.3.4. Aggregating ordinal values: Borda method -- 4.4.4. Other methods -- 4.4.4.1. Game theory-based methods for uncertain situations -- 4.4.4.1.1. Wald's pessimistic method -- 4.4.4.1.2. Hurwicz's optimistic method -- 4.4.4.1.3. Wald-Hurwicz prudent method -- 4.4.4.1.4. Savage maximum regret method -- 4.4.4.2. Pairwise comparison-based methods -- 4.4.4.2.1. Condorcet method -- 4.4.4.2.2. Outranking methods -- 4.5. Two objective functions optimization for advanced control systems -- 4.5.1. Aggregating identification with the design of a dynamical control system -- 4.5.2. Aggregating decision model identification with the supervision -- 4.6. Notes and comments -- 5: Methods and Tools for Model-based Decision-making -- 5.1. Introduction -- 5.2. Introductory examples -- 5.2.1. Choosing a job: probabilistic case -- 5.2.2. Starting a business -- 5.2.3. Selecting an IT engineer -- 5.3. Decisions and decision activities. Basic concepts.
5.3.1. Definition -- 5.3.2. Approaches -- 5.4. Decision analysis -- 5.4.1. Preliminary analysis: preparing the choice -- 5.4.1.1. Setting the objectives -- 5.4.1.2. Assessing the importance of objectives -- 5.4.1.3. Specification of alternatives -- 5.4.1.4. Table of consequences -- 5.4.1.5 Single-dimension value function -- 5.4.2. Making a choice: structuring and solving decision problems -- 5.4.2.1. Graphical tools for structuring decision problems -- 5.4.2.2. Weighted additive method - probabilistic version -- 5.4.2.3. Criteria interacting case -- 5.5. Notes and comments -- 5.6. Other remarks/comments -- 6: Decision-Making - Case Study Simulation -- 6.1. Decision problem in uncertain environment -- 6.2. Problem statement -- 6.3. Simulation principle -- 6.4. Case studies -- 6.4.1. Stock management -- 6.4.2. Competitive tender -- 6.4.3. Queuing process or ATM -- Appendix 1: Uniformly Distributed Pseudo-random Generators -- A1.1. Hardware algorithm -- A1.2. Software algorithm -- A1.3. Properties of (B)PRS -- Appendix 2: Prescribed Distribution Pseudo-Random Generators -- A2.1. Principle of stochastic universal sampling -- A2.2. Baker's genuine algorithm -- A2.3. Baker's generalized algorithm -- A2.4. Examples of generated PRS -- Bibliography -- Index.
Summary: 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.
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Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- List of Acronyms -- Preface -- Acknowledgments -- 1: Metaheuristics - Local Methods -- 1.1. Overview -- 1.2. Monte Carlo principle -- 1.3. Hill climbing -- 1.4. Taboo search -- 1.4.1. Principle -- 1.4.2. Greedy descent algorithm -- 1.4.3. Taboo search method -- 1.4.4. Taboo list -- 1.4.5. Taboo search algorithm -- 1.4.6. Intensification and diversification -- 1.4.7. Application examples -- 1.4.7.1. Searching the smallest value on a table -- 1.4.7.2. The problem of N queens -- 1.5. Simulated annealing -- 1.5.1. Principle of thermal annealing -- 1.5.2. Kirkpatrick's model of thermal annealing -- 1.5.3. Simulated annealing algorithm -- 1.6. Tunneling -- 1.6.1. Tunneling principle -- 1.6.2. Types of tunneling -- 1.6.2.1. Stochastic tunneling -- 1.6.2.2. Tunneling with penalties -- 1.6.3. Tunneling algorithm -- 1.7. GRASP methods -- 2: Metaheuristics - Global Methods -- 2.1. Principle of evolutionary metaheuristics -- 2.2. Genetic algorithms -- 2.2.1. Biology breviary -- 2.2.2. Features of genetic algorithms -- 2.2.2.1. Genetic operations -- 2.2.2.2. Inheritors viability -- 2.2.2.3. Selection for reproduction -- 2.2.2.3.1. Selection by fitness -- 2.2.2.3.2. Selection by σ-normalization -- 2.2.2.3.3. Selection by Boltzmann's law -- 2.2.2.3.4. Selection by ranking -- 2.2.2.3.5. Selection by tournament -- 2.2.2.3.6. Elitist selection -- 2.2.2.4. Selection for survival -- 2.2.2.4.1. Generational selection -- 2.2.2.4.2. Elitist selection -- 2.2.2.4.3. Generational elitist selection -- 2.2.3. General structure of a GA -- 2.2.4. On the convergence of GA -- 2.2.5. How to implement a genetic algorithm -- 2.3. Hill climbing by evolutionary strategies -- 2.3.1. Climbing by the steepest ascent -- 2.3.2. Climbing by the next ascent.

2.3.3. Hill climbing by group of alpinists -- 2.4. Optimization by ant colonies -- 2.4.1. Ant colonies -- 2.4.1.1. Natural ants -- 2.4.1.2. Aspects inspired from natural ants -- 2.4.1.3. Features developed for the artificial ants -- 2.4.2. Basic optimization algorithm by ant colonies -- 2.4.3. Pheromone trail update -- 2.4.3.1. Adaptive delayed update -- 2.4.3.2. On-line update -- 2.4.3.3. Update through elitist strategy -- 2.4.3.4. Update by ants ranking -- 2.4.4. Systemic ant colony algorithm -- 2.4.5. Traveling salesman example -- 2.5. Particle swarm optimization -- 2.5.1. Basic metaheuristic -- 2.5.1.1. Principle -- 2.5.1.2. Particles dynamical model -- 2.5.1.3. Selecting the informants -- 2.5.2. Standard PSO algorithm -- 2.5.3. Adaptive PSO algorithm with evolutionary strategy -- 2.5.4. Fireflies algorithm -- 2.5.4.1. Principle -- 2.5.4.2. Dynamical model of fireflies behavior -- 2.5.4.3. Standard fireflies algorithm -- 2.5.5. Bats algorithm -- 2.5.5.1. Principle -- 2.5.5.2. Dynamical model of bats behavior -- 2.5.5.3. Standard bats algorithm -- 2.5.6. Bees algorithm -- 2.5.6.1. Principle -- 2.5.6.2. Dynamical and cooperative model of bees' behavior -- 2.5.6.3. Standard bee algorithm -- 2.5.7. Multivariable prediction by PSO -- 2.6. Optimization by harmony search -- 2.6.1. Musical composition and optimization -- 2.6.2. Harmony search model -- 2.6.3. Standard harmony search algorithm -- 2.6.4. Application example -- 3: Stochastic Optimization -- 3.1. Introduction -- 3.2. Stochastic optimization problem -- 3.3. Computing the repartition function of a random variable -- 3.4. Statistical criteria for optimality -- 3.4.1. Case of totally admissible solutions -- 3.4.2. Case of partially admissible solutions -- 3.5. Examples -- 3.6. Stochastic optimization through games theory -- 3.6.1. Principle -- 3.6.2. Wald strategy (maximin).

3.6.3. Hurwicz strategy -- 3.6.4. Laplace strategy -- 3.6.5. Bayes-Laplace strategy -- 3.6.6. Savage strategy -- 3.6.7. Example -- 4: Multi-Criteria Optimization -- 4.1. Introduction -- 4.2. Introductory examples -- 4.2.1. Choosing the first job -- 4.2.2. Selecting an IT tool -- 4.2.3. Setting the production rate of a continuous process plant -- 4.3. Multi-criteria optimization problems -- 4.3.1. Two subclasses of problems -- 4.3.1.1. Multi-attribute problem subclass -- 4.3.2. Dominance and Pareto optimality -- 4.4. Model solving methods -- 4.4.1. Classifications -- 4.4.2. Substitution-based methods -- 4.4.2.1. Setting additional constraints -- 4.4.2.2. Goal programming -- 4.4.2.3. Progressive solving -- 4.4.2.3.1. Method steps (minimization case) -- 4.4.3. Aggregation-based methods -- 4.4.3.1. Definition and requirements -- 4.4.3.2. Simple weighted averaging method -- 4.4.3.3. Distance-based methods -- 4.4.3.4. Aggregating ordinal values: Borda method -- 4.4.4. Other methods -- 4.4.4.1. Game theory-based methods for uncertain situations -- 4.4.4.1.1. Wald's pessimistic method -- 4.4.4.1.2. Hurwicz's optimistic method -- 4.4.4.1.3. Wald-Hurwicz prudent method -- 4.4.4.1.4. Savage maximum regret method -- 4.4.4.2. Pairwise comparison-based methods -- 4.4.4.2.1. Condorcet method -- 4.4.4.2.2. Outranking methods -- 4.5. Two objective functions optimization for advanced control systems -- 4.5.1. Aggregating identification with the design of a dynamical control system -- 4.5.2. Aggregating decision model identification with the supervision -- 4.6. Notes and comments -- 5: Methods and Tools for Model-based Decision-making -- 5.1. Introduction -- 5.2. Introductory examples -- 5.2.1. Choosing a job: probabilistic case -- 5.2.2. Starting a business -- 5.2.3. Selecting an IT engineer -- 5.3. Decisions and decision activities. Basic concepts.

5.3.1. Definition -- 5.3.2. Approaches -- 5.4. Decision analysis -- 5.4.1. Preliminary analysis: preparing the choice -- 5.4.1.1. Setting the objectives -- 5.4.1.2. Assessing the importance of objectives -- 5.4.1.3. Specification of alternatives -- 5.4.1.4. Table of consequences -- 5.4.1.5 Single-dimension value function -- 5.4.2. Making a choice: structuring and solving decision problems -- 5.4.2.1. Graphical tools for structuring decision problems -- 5.4.2.2. Weighted additive method - probabilistic version -- 5.4.2.3. Criteria interacting case -- 5.5. Notes and comments -- 5.6. Other remarks/comments -- 6: Decision-Making - Case Study Simulation -- 6.1. Decision problem in uncertain environment -- 6.2. Problem statement -- 6.3. Simulation principle -- 6.4. Case studies -- 6.4.1. Stock management -- 6.4.2. Competitive tender -- 6.4.3. Queuing process or ATM -- Appendix 1: Uniformly Distributed Pseudo-random Generators -- A1.1. Hardware algorithm -- A1.2. Software algorithm -- A1.3. Properties of (B)PRS -- Appendix 2: Prescribed Distribution Pseudo-Random Generators -- A2.1. Principle of stochastic universal sampling -- A2.2. Baker's genuine algorithm -- A2.3. Baker's generalized algorithm -- A2.4. Examples of generated PRS -- Bibliography -- Index.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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