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Bioinspired Heuristics for Optimization

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bioinspired Heuristics for Optimization/ edited by El-Ghazali Talbi, Amir Nakib.
other author:	Talbi, El-Ghazali.
Description:	VIII, 314 p. 97 illus., 52 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-95104-1
ISBN:	9783319951041

Bioinspired Heuristics for Optimization
Bioinspired Heuristics for Optimization[electronic resource] /edited by El-Ghazali Talbi, Amir Nakib. - 1st ed. 2019. - VIII, 314 p. 97 illus., 52 illus. in color.online resource. - Studies in Computational Intelligence,7741860-949X ;. - Studies in Computational Intelligence,564.

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 classiﬁer for the adaptive ACS-TSP pheromone parameters.

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.

ISBN: 9783319951041

Standard No.: 10.1007/978-3-319-95104-1doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Bioinspired Heuristics for Optimization
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