The aim of Metaheuristics: Progress in Complex Systems Optimization 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.
Highlighted are recent developments in the areas of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms.
表中的内容
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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