Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization.- Four-bar Mechanism Synthesis 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.- A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation.- An Ant-bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions.- Dynamic Load Balancing Using an Ant Colony Approach in Micro-cellular Mobile Communications Systems.- New Ways to Calibrate Evolutionary Algorithms.- Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms.- Local Search Based on Genetic Algorithms.- Designing Efficient Evolutionary Algorithms for Cluster Optimization: A Study on Locality.- Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm.- Evolutionary Generation of Artificial Creature’s Personality for Ubiquitous Services.- 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 Using a Genetic Algorithm: An Aeronautic Military Application.- Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms.- Making a Difference to Differential Evolution.- Hidden Markov Models Training Using Population-based Metaheuristics.- Inequalities and Target Objectives for Metaheuristic Search – Part I: Mixed Binary Optimization.

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.

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