£86.98

Springer Hybrid Metaheuristics: An Emerging Approach to Optimization

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Description

Optimization problems are essential across many different fields. This volume from the Studies in Computational Intelligence series explores how these problems can be tackled using approximate algorithms like metaheuristics. While traditional methods such as simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization are effective, there is a growing need for more advanced solutions. This book examines the rise of hybrid metaheuristics, which involve the skilled combination of a metaheuristic with other optimization techniques. By merging different methods, researchers can achieve more efficient behavior and higher flexibility. This approach allows users to combine the specific advantages of various algorithms to solve complex problems more effectively. For professionals and students in operational research and computational intelligence, this text provides a look into how these combined techniques are shaping the future of optimization.

Key Features

Explores the application of metaheuristics like simulated annealing and tabu search to solve important optimization problems.

Covers various algorithmic methods including evolutionary computation, iterated local search, and ant colony optimization.

Discusses the benefits of hybrid metaheuristics for achieving more efficient behavior in complex problem solving.

Provides insights into how combining different optimization techniques increases overall flexibility in computational intelligence.

Part of the Studies in Computational Intelligence series, volume 114, published by Springer.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
11 April 2008
Listed Since
28 January 2008

Barcode

No barcode data available

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