Detailansicht

InhaltsangabeComparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization.- Linkage Synthesis of a Four-Bar Mechanism for n Desired Path Points Using Simulated Annealing.- MOSS-II Tabu/Scatter Search for Nonlinear Multiobjective Optimization.- Feature Selection for Heterogeneous Ensembles of Nearest Neighbour Classifiers Using Hybrid Tabu Search.- Parallel Ant Colony Optimization Algorithm for Solving Continuous Type Engineering Problems.- An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions.- Dynamic Load Balancing Using an Ant Colony Approach in Microcellular Systems.- How to Calibrate Evolutionary Algorithms.- Divide and Evolve: A Sequential Hybridization Strategy Using Evolutionary Algorithms.- Evolvable Artificial Creature.- Local Search Based on Genetic Algorithms.- A Study on Locality and Heritability in Hybrid Evolutionary Cluster Optimization.- Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm.- Some Guidelines for Genetic Algorithm implementation in MINLP Batch Plant Design Problems.- Coevolutionary Genetic Algorithm to Solve Economic Dispatch.- An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem.- Optimizing Stochastic Functions by Using Genetic Algorithm: An Aeronautic Military Application.- Learning Structure Illuminates Black Boxes: An Introduction into Estimation of Distribution Algorithms.- Making a Difference to Differential Evolution.- Hidden Markov Models Training Using Population-Based Metaheuristics.- New Metaheuristic Approaches in Data Mining

Many advances have been made recently in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general-purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and various metaheuristics. The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications. This book is suitable for practitioners, researchers and graduate students in disciplines such as optimization, heuristics, operations research, and natural computing.