 Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
ISBN: 978-0-7695-0100-0
Paperback
410 pages
February 2000, Wiley-IEEE Computer Society Press
US $94.50
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Preface.
1. Introduction.
1.1 Combinatorial Optimization.
1.2 Optimization Methods.
1.3 States, Moves, and Optimality.
1.4 Local Search.
1.5 Optimal versus Final Solution.
1.6 Single versus Multicriteria Constrained Optimization.
1.7 Convergence Analysis of Iterative Algorithms.
1.8 Markov Chains.
1.9 Parallel Processing.
1.10 Summary and Organization of the Book.
References.
Exercises.
2. Simulated Annealing (SA).
2.1 Introduction.
2.2 Simulated Annealing Algorithm.
2.3 SA Convergence Aspects.
2.4 Parameters of the SA Algorithm.
2.5 SA Requirements.
2.6 SA Applications.
2.7 Parallelization of SA.
2.8 Conclusions and Recent Work.
References.
Exercises.
3. Genetic Algorithms (GAs).
3.1 Introduction.
3.2 Genetic Algorithm.
3.3 Schema Theorem and Implicit Parallelism.
3.4 GA Convergence Aspects.
3.5 GA in Practice.
3.6 Parameters of GAs.
3.7 Applications of GAs.
3.8 Parallelization of GA.
3.9 Other Issues and Recent Work.
3.10 Conclusions.
References.
Exercises.
4. Tabu Search (TS).
4.1 Introduction.
4.2 Tabu Search Algorithm.
4.3 Implementation-Related Issues.
4.4 Limitations of Short-Term Memory.
4.5 Examples of Diversifying Search.
4.6 TS Convergence Aspects.
4.7 TS Applications.
4.8 Parallelization of TS.
4.9 Other Issues and Related Work.
4.10 Conclusions.
References.
Exercises.
5. Simulated Evolution (SimE).
5.1 Introduction.
5.2 Historical Background.
5.3 Simulated Evolution Algorithm.
5.4 SimE Operators and Parameters.
5.5 Comparison of SimE, SA, and GA.
5.6 SimE Convergence Aspects.
5.7 SimE Applications.
5.8 Parallelization of SimE.
5.9 Conclusions and Recent Work.
References.
Exercises.
6. Stochastic Evolution (StocE).
6.1 Introduction.
6.2 Historical Background.
6.3 Stochastic Evolution Algorithm.
6.4 Stochastic Evolution Convergence Aspects.
6.5 Stochastic Evolution Applications.
6.6 Parallelization of Stochastic Evolution.
6.7 Conclusions and Recent Work.
References.
Exercises.
7. Hybrids and Other Issues.
7.1 Introduction.
7.2 Overview of Algorithms.
7.3 Hybridization.
7.4 GA and Multiobjective Optimization.
7.5 Fuzzy Logic for Multiobjective Optimization.
7.6 Artificial Neural Networks.
7.7 Quality of the Solution.
7.8 Conclusions.
References.
Exercises.
About the Authors.
Index.
1. Introduction.
1.1 Combinatorial Optimization.
1.2 Optimization Methods.
1.3 States, Moves, and Optimality.
1.4 Local Search.
1.5 Optimal versus Final Solution.
1.6 Single versus Multicriteria Constrained Optimization.
1.7 Convergence Analysis of Iterative Algorithms.
1.8 Markov Chains.
1.9 Parallel Processing.
1.10 Summary and Organization of the Book.
References.
Exercises.
2. Simulated Annealing (SA).
2.1 Introduction.
2.2 Simulated Annealing Algorithm.
2.3 SA Convergence Aspects.
2.4 Parameters of the SA Algorithm.
2.5 SA Requirements.
2.6 SA Applications.
2.7 Parallelization of SA.
2.8 Conclusions and Recent Work.
References.
Exercises.
3. Genetic Algorithms (GAs).
3.1 Introduction.
3.2 Genetic Algorithm.
3.3 Schema Theorem and Implicit Parallelism.
3.4 GA Convergence Aspects.
3.5 GA in Practice.
3.6 Parameters of GAs.
3.7 Applications of GAs.
3.8 Parallelization of GA.
3.9 Other Issues and Recent Work.
3.10 Conclusions.
References.
Exercises.
4. Tabu Search (TS).
4.1 Introduction.
4.2 Tabu Search Algorithm.
4.3 Implementation-Related Issues.
4.4 Limitations of Short-Term Memory.
4.5 Examples of Diversifying Search.
4.6 TS Convergence Aspects.
4.7 TS Applications.
4.8 Parallelization of TS.
4.9 Other Issues and Related Work.
4.10 Conclusions.
References.
Exercises.
5. Simulated Evolution (SimE).
5.1 Introduction.
5.2 Historical Background.
5.3 Simulated Evolution Algorithm.
5.4 SimE Operators and Parameters.
5.5 Comparison of SimE, SA, and GA.
5.6 SimE Convergence Aspects.
5.7 SimE Applications.
5.8 Parallelization of SimE.
5.9 Conclusions and Recent Work.
References.
Exercises.
6. Stochastic Evolution (StocE).
6.1 Introduction.
6.2 Historical Background.
6.3 Stochastic Evolution Algorithm.
6.4 Stochastic Evolution Convergence Aspects.
6.5 Stochastic Evolution Applications.
6.6 Parallelization of Stochastic Evolution.
6.7 Conclusions and Recent Work.
References.
Exercises.
7. Hybrids and Other Issues.
7.1 Introduction.
7.2 Overview of Algorithms.
7.3 Hybridization.
7.4 GA and Multiobjective Optimization.
7.5 Fuzzy Logic for Multiobjective Optimization.
7.6 Artificial Neural Networks.
7.7 Quality of the Solution.
7.8 Conclusions.
References.
Exercises.
About the Authors.
Index.
