## Iterative computer algorithms with applications in engineering: solving combinatorial optimization problemsIterative 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 NP-hard problems. 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 |

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### Contents

Simulated Annealing SA | 49 |

Genetic Algorithms GAs | 109 |

Tabu Search TS | 183 |

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### Common terms and phrases

accepted allocation applied approach aspiration criterion best solution Boolean network candidate list cell chain-set Chapter chromosome circuit combinatorial optimization combinatorial optimization problems Computer Computer-Aided Design configuration graph convergence cost function Cost(S crossover current solution cycle defined Design Automation diversification edges Endlf Equation evaluation example fanout node Figure fitness values FPGA fuzzy logic genetic algorithms given global Hamiltonian cycle heuristic IEEE implementation individual initial solution input length Markov chain matrix minimization modules move multiobjective optimization mutation neighbor neighborhood number of iterations objective function offspring optimal solution optimum parallel parameter parents partition path performance placement population probability procedure processors quadratic assignment problem random runtime schema Section selected serializable SimE algorithm simulated annealing simulated annealing algorithm simulated evolution solve speed-up stochastic evolution strategy string subset swap tabu list tabu restriction tabu search techniques temperature tion transition traveling salesman problem tree trial updated VLSI weight wire-length