## Handbook of MetaheuristicsFred W. Glover, Gary A. Kochenberger Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. Over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic al- rithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by variously chosen rules of exchange popularly given the name of “crossover. ” In other population-based methods, based on the notion of path relinking, neighborhood structures are used in their full generality, including constructive and destructive neighborhoods as well as those for transitioning between (complete) solutions. Certain hybrids of classical evoluti- ary approaches, which link them with local search, also use neighborhood structures more fully, though apart from the combination process itself. |

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### Contents

An Introduction to Tabu Search 37 | 36 |

Genetic Algorithms | 55 |

Automatic Synthesis of Topologies | 83 |

A Gentle Introduction to Memetic Algorithms | 105 |

Variable Neighborhood Search | 145 |

Guided Local Search | 185 |

Greedy Randomized Adaptive Search Procedures | 219 |

Algorithms | 251 |

MultiStart Methods 355 | 354 |

Local Search and Constraint Programming | 369 |

Constraint Satisfaction | 405 |

Artificial Neural Networks for Combinatorial Optimization | 429 |

an Emerging Direction | 456 |

Parallel Strategies for Metaheuristics | 475 |

Metaheuristic Class Libraries | 515 |

Asynchronous Teams 537 | 536 |

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ACO algorithms agents applications approach assignment problem best solution combinatorial optimization combinatorial optimization problems components Computer Science constraint programming construction convergence cost crossover current solution defined diversification domain Dorigo evaluation Evolutionary Computation example exploration feasible solutions Gendreau genetic algorithm genetic programming global optimization Glover graph GRASP greedy heuristic Hopfield hybrid hyper-heuristic IEEE implementation improvement initial solution instances iterated local search Journal local optimum local search metaheuristics minimize moves neighborhood structure nodes objective function obtained ofthe Operations Research optimal solution parallel parameter path relinking performance permutation perturbation pheromone pheromone trails population Proceedings processors quadratic assignment problem random recombination Resende restart Ribeiro scatter search scheduling problem search algorithm search methods search procedure search space selection sequence simulated annealing algorithm solution space solving stochastic strategies sub-neighborhoods subset tabu search techniques traveling salesman problem update vehicle routing problem vertex