Iterative Computer Algorithms with Applications in Engineering
Solving Combinatorial Optimization Problems
1st Edition
Published on 27. January 2000
Book
Paperback/Softback
416 pages
978-0-7695-0100-0 (ISBN)
Description
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
More details
Series
Language
English
Place of publication
United States
Publishing group
I.E.E.E.Press
Target group
College/higher education
Professional and scholarly
Edition type
Annotated edition
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 24 mm
Weight
672 gr
ISBN-13
978-0-7695-0100-0 (9780769501000)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Sadiq M. Sait obtained a Bachelor's degree in Electronics from Bangalore University, India, in 1981, and master's and Ph.D. degrees in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a professor in the Department of Computer Engineering of KFUPM. Sait has authored over 85 research papers in international journals and conferences. He is coauthor of the book VLSI Physical Design Automation: Theory and Practice, published in January 1995. He has also contributed two chapters to a book entitled Progress in VLSI design. He served on the editorial board of International Journal of Computer-Aided Design between 1988 and 1990. Currently he is the editor of Arabian Journal for Science and Engineering for Computer Science & Engineering. His current areas of interest are in digital design automation, VLSI system design, high-level synthesis, and iterative algorithms.
Habib Youssef received a Diplome d'Ingenieur en Informatique from the Faculté des Sciences de Tunis in 1982 and a Ph.D. in Computer Science from the University of Minnesota in 1990. He is currently and Associate Professor of Computer Engineering at King Fahd University of Petroleum and Minerals, Saudi Arabia. Youssef has authored more than 45 journal and conference papers. He is the coauthor of the book VLSI Physical Design Automation: Theory and Practice, January 1995. His main research interests are CAD of VLSI, computer networks, and performance evaluation of computer systems, and general stochastic and evolutionary algorithms.
Content
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.