Optimization in Engineering Sciences

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.

• LIST OF FIGURES ixLIST OF TABLES xiiiLIST OF ALGORITHMS xvLIST OF ACRONYMS xviiPREFACE xixACKNOWLEDGEMENTS xxiCHAPTER 1. METAHEURISTICS – LOCAL METHODS 11.1. Overview 11.2. Monte Carlo principle 61.3. Hill climbing 121.4. Taboo search 201.4.1. Principle 201.4.2. Greedy descent algorithm 201.4.3. Taboo search method 231.4.4. Taboo list 251.4.5. Taboo search algorithm 261.4.6. Intensification and diversification 301.4.7. Application examples 311.5. Simulated annealing 391.5.1. Principle of thermal annealing 391.5.2. Kirkpatrick’s model of thermal annealing 411.5.3. Simulated annealing algorithm 431.6. Tunneling 461.6.1. Tunneling principle 461.6.2. Types of tunneling 481.6.3. Tunneling algorithm 491.7. GRASP methods 51CHAPTER 2. METAHEURISTICS – GLOBAL METHODS 532.1. Principle of evolutionary metaheuristics 532.2. Genetic algorithms 552.2.1. Biology breviary 552.2.2. Features of genetic algorithms 572.2.3. General structure of a GA 732.2.4. On the convergence of GA 772.2.5. How to implement a genetic algorithm 842.3. Hill climbing by evolutionary strategies 1002.3.1. Climbing by the steepest ascent 1012.3.2. Climbing by the next ascent 1042.3.3. Hill climbing by group of alpinists 1062.4. Optimization by ant colonies 1072.4.1. Ant colonies 1072.4.2. Basic optimization algorithm by ant colonies 1102.4.3. Pheromone trail update 1182.4.4. Systemic ant colony algorithm 1222.4.5. Traveling salesman example 1282.5. Particle swarm optimization 1322.5.1. Basic metaheuristic 1322.5.2. Standard PSO algorithm 1412.5.3. Adaptive PSO algorithm with evolutionary strategy 1462.5.4. Fireflies algorithm 1632.5.5. Bats algorithm 1732.5.6. Bees algorithm 1822.5.7. Multivariable prediction by PSO 1942.6. Optimization by harmony search 2072.6.1. Musical composition and optimization 2072.6.2. Harmony search model 2082.6.3. Standard harmony search algorithm 2122.6.4. Application example 215CHAPTER 3. STOCHASTIC OPTIMIZATION 2193.1. Introduction 2193.2. Stochastic optimization problem 2213.3. Computing the repartition function of a random variable 2223.4. Statistical criteria for optimality 2303.4.1. Case of totally admissible solutions 2313.4.2. Case of partially admissible solutions 2343.5. Examples 2403.6. Stochastic optimization through games theory 2453.6.1. Principle 2453.6.2. Wald strategy (maximin) 2473.6.3. Hurwicz strategy 2483.6.4. Laplace strategy 2493.6.5. Bayes–Laplace strategy 2493.6.6. Savage strategy 2503.6.7. Example 251CHAPTER 4. MULTI-CRITERIA OPTIMIZATION 2534.1. Introduction 2534.2. Introductory examples 2554.2.1. Choosing the first job 2554.2.2. Selecting an IT tool 2564.2.3. Setting the production rate of a continuous process plant 2564.3. Multi-criteria optimization problems 2574.3.1. Two subclasses of problems 2574.3.2. Dominance and Pareto optimality 2624.4. Model solving methods 2654.4.1. Classifications 2654.4.2. Substitution-based methods 2664.4.3. Aggregation-based methods 2704.4.4. Other methods 2824.5. Two objective functions optimization for advanced control systems 2924.5.1. Aggregating identification with the design of a dynamical control system 2924.5.2. Aggregating decision model identification with the supervision 3024.6. Notes and comments 307CHAPTER 5. METHODS AND TOOLS FOR MODEL-BASED DECISION-MAKING 3095.1. Introduction 3095.2. Introductory examples 3105.2.1. Choosing a job: probabilistic case 3105.2.2. Starting a business 3115.2.3. Selecting an IT engineer 3115.3. Decisions and decision activities. 5.3.1. Definition 3135.3.2. Approaches 3145.4. Decision analysis 3165.4.1. Preliminary analysis: preparing the choice 3175.4.2. Making a choice: structuring and solving decision problems 3305.5. Notes and comments 3475.6. Other remarks/comments 347CHAPTER 6. DECISION-MAKING – CASE STUDY SIMULATION 3516.1. Decision problem in uncertain environment 3516.2. Problem statement 3526.3. Simulation principle 3536.4. Case studies 3576.4.1. Stock management 3586.4.2. Competitive tender 3626.4.3. Queuing process or ATM 365APPENDIX 1 369APPENDIX 2 377BIBLIOGRAPHY 393INDEX 413