Ebook: Advances in Metaheuristics for Hard Optimization
- Tags: Artificial Intelligence (incl. Robotics), Optimization, Operations Research Mathematical Programming, Engineering Design, Theory of Computation
- Series: Natural Computing Series
- Year: 2008
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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 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 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.
Content:
Front Matter....Pages I-XV
Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization....Pages 1-22
Four-bar Mechanism Synthesis for n Desired Path Points Using Simulated Annealing....Pages 23-37
“MOSS-II” Tabu/Scatter Search for Nonlinear Multiobjective Optimization....Pages 39-67
Feature Selection for Heterogeneous Ensembles of Nearest-neighbour Classifiers Using Hybrid Tabu Search....Pages 69-85
A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation....Pages 87-110
An Ant-bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions....Pages 111-136
Dynamic Load Balancing Using an Ant Colony Approach in Micro-cellular Mobile Communications Systems....Pages 137-152
New Ways to Calibrate Evolutionary Algorithms....Pages 153-177
Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms....Pages 179-198
Local Search Based on Genetic Algorithms....Pages 199-221
Designing Efficient Evolutionary Algorithms for Cluster Optimization: A Study on Locality....Pages 223-250
Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm....Pages 251-261
Evolutionary Generation of Artificial Creature’s Personality for Ubiquitous Services....Pages 263-292
Some Guidelines for Genetic Algorithm Implementation in MINLP Batch Plant Design Problems....Pages 293-315
Coevolutionary Genetic Algorithm to Solve Economic Dispatch....Pages 317-327
An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem....Pages 329-351
Optimizing Stochastic Functions Using a Genetic Algorithm: An Aeronautic Military Application....Pages 353-363
Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms....Pages 365-395
Making a Difference to Differential Evolution....Pages 397-414
Hidden Markov Models Training Using Population-based Metaheuristics....Pages 415-438
Back Matter....Pages 475-480
Inequalities and Target Objectives for Metaheuristic Search – Part I: Mixed Binary Optimization....Pages 439-474
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.
Content:
Front Matter....Pages I-XV
Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization....Pages 1-22
Four-bar Mechanism Synthesis for n Desired Path Points Using Simulated Annealing....Pages 23-37
“MOSS-II” Tabu/Scatter Search for Nonlinear Multiobjective Optimization....Pages 39-67
Feature Selection for Heterogeneous Ensembles of Nearest-neighbour Classifiers Using Hybrid Tabu Search....Pages 69-85
A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation....Pages 87-110
An Ant-bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions....Pages 111-136
Dynamic Load Balancing Using an Ant Colony Approach in Micro-cellular Mobile Communications Systems....Pages 137-152
New Ways to Calibrate Evolutionary Algorithms....Pages 153-177
Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms....Pages 179-198
Local Search Based on Genetic Algorithms....Pages 199-221
Designing Efficient Evolutionary Algorithms for Cluster Optimization: A Study on Locality....Pages 223-250
Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm....Pages 251-261
Evolutionary Generation of Artificial Creature’s Personality for Ubiquitous Services....Pages 263-292
Some Guidelines for Genetic Algorithm Implementation in MINLP Batch Plant Design Problems....Pages 293-315
Coevolutionary Genetic Algorithm to Solve Economic Dispatch....Pages 317-327
An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem....Pages 329-351
Optimizing Stochastic Functions Using a Genetic Algorithm: An Aeronautic Military Application....Pages 353-363
Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms....Pages 365-395
Making a Difference to Differential Evolution....Pages 397-414
Hidden Markov Models Training Using Population-based Metaheuristics....Pages 415-438
Back Matter....Pages 475-480
Inequalities and Target Objectives for Metaheuristic Search – Part I: Mixed Binary Optimization....Pages 439-474
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