Metaheuristics : Progress in Complex Systems Optimization
Author(s)
Bibliographic Information
Metaheuristics : Progress in Complex Systems Optimization
（Operations research/computer science interface series, 39）
Springer, c2007
Available at 6 libraries
Description and Table of Contents
Description
This book's aim is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.
Table of Contents
Scatter Search.- Experiments Using Scatter Search for the Multidemand Multidimensional Knapsack Problem.- A Scatter Search Heuristic for the Fixed-Charge Capacitated Network Design Problem.- Tabu Search.- Tabu Search-Based Metaheuristic Algorithm for Large-scale Set Covering Problems.- Log-Truck Scheduling with a Tabu Search Strategy.- Nature-inspired methods.- Solving the Capacitated Multi-Facility Weber Problem by Simulated Annealing, Threshold Accepting and Genetic Algorithms.- Reviewer Assignment for Scientific Articles using Memetic Algorithms.- GRASP and Iterative Methods.- Grasp with Path-Relinking for the Tsp.- Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for the University Course Timetabling Problem.- Dynamic and Stochastic Problems.- Variable Neighborhood Search for the Probabilistic Satisfiability Problem.- The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty.- Adaptive Control of Genetic Parameters for Dynamic Combinatorial Problems.- A Memetic Algorithm for Dynamic Location Problems.- A Study of Canonical GAs for NSOPs.- Particle Swarm Optimization and Sequential Sampling in Noisy Environments.- Distributed and Parallel Algorithms.- Embedding a Chained Lin-Kernighan Algorithm into a Distributed Algorithm.- Exploring Grid Implementations of Parallel Cooperative Metaheuristics.- Algorithm Tuning, Algorithm Design and Software Tools.- Using Experimental Design to Analyze Stochastic Local Search Algorithms for Multiobjective Problems.- Distance Measures and Fitness-Distance Analysis for the Capacitated Vehicle Routing Problem.- Tuning Tabu Search Strategies Via Visual Diagnosis.- Solving Vehicle Routing Using IOPT.
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