Iterative Computer Algorithms with Applications in Engineering

This book examines one class of combinatorial optimization algorithms -- general iterative non-deterministic algorithms. These algorithms have recently shown significant interest due to their generality, ease of implementation, and the many success stories reporting very positive results.Iterative Computer Algorithms and Their Applications in Engineering uniformly describes five iterative algorithms for solving hard combinatorial optimization problems, namely simulated annealing, genetic algorithms, Tabu search, simulated evolution, and stochastic evolution. It is the only book to describe in a single volume these five main iterative combinatorial algorithms. The introductory chapter motivates the reader to study and use the general iterative approximation algorithms, while introducing the basic terminology.The authors present various iterative techniques and illustrate how they can be applied to solve several NP-hard problems. The book includes case studies of real engineering problems and provides comparative analysis using various techniques that the authors have experimented with and solved. For each algorithm, the authors present the procedures of the algorithm, parameter selection criteria, convergence property analysis, and parallelization. There are also several real-world examples that illustrate various aspects of the algorithms, including real engineering problems. Examples are presented wherever appropriate to illustrate any required mathematical concepts.The book has many unique features An integrated and up-to-date description of iterative non-deterministic algorithms It is the first book to describe in detail simulated evolution and stochasticevolution A brief introduction to fuzzy logic and its application in the formulation of multi-objective optimization problems A discussion on hybrid techniques that combine features of heuristics discussed in the book A level of treatment suitable for first year graduate stu

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.