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Springer Hybrid Metaheuristics: Optimization Algorithms - Springer Vol. 114

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Description

Hybrid Metaheuristics explores the powerful combination of metaheuristic algorithms with complementary optimization techniques to solve complex problems across multiple fields. This volume examines how skilled integration of methods such as simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighbourhood search, and ant colony optimization delivers superior efficiency and flexibility compared to standalone approaches. Hybrid metaheuristics leverage the distinct advantages of each technique to overcome limitations and achieve faster convergence on challenging optimization problems. Part of Springer's Studies in Computational Intelligence series (volume 114), this resource provides comprehensive coverage of how modern optimisation combines approximate algorithms with other methods to create more robust solutions.

Key Features

Hybrid Metaheuristics combines metaheuristic algorithms with other optimization techniques to improve solution efficiency and adaptability.

Covers multiple metaheuristic approaches including simulated annealing, tabu search, evolutionary computation, and ant colony optimization.

Explores iterated local search and variable neighbourhood search strategies within the framework of hybrid optimization methods.

Part of Springer's Studies in Computational Intelligence series, volume 114, bringing academic rigour to operational research.

Product Specifications

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

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