Description
Springer Advances In Metaheuristics For Hard Optimization by Patrick Siarry
Many advances have recently been made 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 metaheuristics._x000D_ _x000D_
Comparison 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_x000D_