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Springer Nature-Inspired Algorithms and Applied Optimization - Springer

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Description

This comprehensive book examines state-of-the-art developments in nature-inspired algorithms and their practical applications across engineering design optimization, feature selection, scheduling, and vehicle routing problems. The work introduces each algorithm with detailed implementation guidance supported by real-world case studies and thorough literature reviews. Readers benefit from self-contained chapters offering theoretical foundations, including convergence analysis and no-free-lunch theorems that illuminate how modern nature-inspired optimization algorithms function. Coverage spans ant colony optimization and additional nature-based computational methods, making this Springer publication an essential reference for researchers, practitioners, and students seeking to understand both the theory and application of bio-inspired optimisation techniques in solving complex computational challenges.

Key Features

Nature-inspired algorithms book from Springer covering practical applications in engineering design, feature selection, scheduling, and vehicle routing.

Includes case studies and extensive literature reviews alongside algorithm introductions and implementation guidance for each technique.

Features self-contained chapters with theoretical analyses including convergence analysis and no-free-lunch theorems for deeper algorithmic understanding.

Covers ant colony optimization and additional bio-inspired optimisation methods used in computational intelligence and applied problem-solving.

Part of the Studies in Computational Intelligence series (volume 744), providing scholarly depth for academic and professional audiences.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
18 October 2017
Listed Since
09 August 2017

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