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…mehr
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.
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 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.
Inhaltsangabe
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.
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.
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