Iterative Computer Algorithms with Applications in Engineering
describes in-depth the five main iterative algorithms for solving
hard combinatorial optimization problems: Simulated Annealing,
Genetic Algorithms, Tabu Search, Simulated Evolution, and
Stochastic Evolution. The authors present various iterative
techniques and illustrate how they can be applied to solve several
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. The
book includes an 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, a survey of recent research work, and examples that
illustrate required mathematical concepts.
The unique features of this book are: An integrated and up-to-date
description of iterative non-deterministic algorithms; Detailed
descriptions of Simulated Evolution and Stochastic Evolution; A
level of treatment suitable for first year graduate student and
practicing engineers; Parallelization aspects and particular
parallel implementations; A brief survey of recent research work;
Graded exercises and an annotated bibliography in each chapter
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.
2. Simulated Annealing (SA).
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.
3. Genetic Algorithms (GAs).
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.
4. Tabu Search (TS).
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.
5. Simulated Evolution (SimE).
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.
6. Stochastic Evolution (StocE).
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.
7. Hybrids and Other Issues.
7.2 Overview of Algorithms.
7.4 GA and Multiobjective Optimization.
7.5 Fuzzy Logic for Multiobjective Optimization.
7.6 Artificial Neural Networks.
7.7 Quality of the Solution.
About the Authors.
Number Of Pages: 410
Published: 10th February 2000
Publisher: INST OF ELECTRICAL & ELEC
Country of Publication: US
Dimensions (cm): 23.05 x 15.2
Weight (kg): 0.6
Edition Number: 1
Edition Type: Annotated