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Solving complex optimization problems with parallel metaheuristics
Parallel Metaheuristics brings together an international group of
experts in parallelism and metaheuristics to provide a much-needed
synthesis of these two fields. Readers discover how metaheuristic
techniques can provide useful and practical solutions for a wide
range of problems and application domains, with an emphasis on the
fields of telecommunications and bioinformatics. This volume fills
a long-existing gap, allowing researchers and practitioners to
develop efficient metaheuristic algorithms to find solutions.
The book is divided into three parts:
* Part One: Introduction to Metaheuristics and Parallelism,
including an Introduction to Metaheuristic Techniques, Measuring
the Performance of Parallel Metaheuristics, New Technologies in
Parallelism, and a head-to-head discussion on Metaheuristics and
Parallelism
* Part Two: Parallel Metaheuristic Models, including Parallel
Genetic Algorithms, Parallel Genetic Programming, Parallel
Evolution Strategies, Parallel Ant Colony Algorithms, Parallel
Estimation of Distribution Algorithms, Parallel Scatter Search,
Parallel Variable Neighborhood Search, Parallel Simulated
Annealing, Parallel Tabu Search, Parallel GRASP, Parallel Hybrid
Metaheuristics, Parallel Multi-Objective Optimization, and Parallel
Heterogeneous Metaheuristics
* Part Three: Theory and Applications, including Theory of Parallel
Genetic Algorithms, Parallel Metaheuristics Applications, Parallel
Metaheuristics in Telecommunications, and a final chapter on
Bioinformatics and Parallel Metaheuristics
Each self-contained chapter begins with clear overviews and
introductions that bring the reader up to speed, describes basic
techniques, and ends with a reference list for further study.
Packed with numerous tables and figures to illustrate the complex
theory and processes, this comprehensive volume also includes
numerous practical real-world optimization problems and their
solutions.
This is essential reading for students and researchers in computer
science, mathematics, and engineering who deal with parallelism,
metaheuristics, and optimization in general.
Real-world problems and modern optimization techniques to solve
them
Here, a team of international experts brings together core ideas
for solving complex problems in optimization across a wide variety
of real-world settings, including computer science, engineering,
transportation, telecommunications, and bioinformatics.
Part One--covers methodologies for complex problem solving
including genetic programming, neural networks, genetic algorithms,
hybrid evolutionary algorithms, and more.
Part Two--delves into applications including DNA sequencing and
reconstruction, location of antennae in telecommunication networks,
metaheuristics, FPGAs, problems arising in telecommunication
networks, image processing, time series prediction, and more.
All chapters contain examples that illustrate the applications
themselves as well as the actual performance of the
algorithms.?Optimization Techniques for Solving Complex Problems is
a valuable resource for practitioners and researchers who work with
optimization in real-world settings.
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