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.