Bioinspired Heuristics for Optimization
Published on 22. December 2018
Book
Paperback/Softback
VIII, 314 pages
978-3-030-06978-0 (ISBN)
Description
This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.
More details
Series
Edition
Softcover Reprint of the Original 1st 2019 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
52 farbige Abbildungen, 45 s/w Abbildungen
VIII, 314 p. 97 illus., 52 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 18 mm
Weight
493 gr
ISBN-13
978-3-030-06978-0 (9783030069780)
DOI
10.1007/978-3-319-95104-1
Schweitzer Classification
Other editions
Additional editions
El-Ghazali Talbi | Amir Nakib
Bioinspired Heuristics for Optimization
Book
08/2018
Springer
€106.99
Shipment within 10-15 days
Content
Possibilistic Framework for Multi-objective Optimization under Uncertainty.- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms.- Intrusion Detection System based on a behavioral approach.- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel.- Storage yard management: modelling and solving.- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning.- Application of genetic algorithm for solving bilevel linear programming problems.- Adapted Bin-Packing algorithm for the yard optimization problem.- Hidden Markov Model classi?er for the adaptive ACS-TSP pheromone parameters.