Bioinspired Heuristics for Optimization

1. Auflage 2019
ISBN: 978-3-319-95103-4
Verlag: Springer International Publishing

Buch, Englisch, Band 774, 314 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 653 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Nakib, Amir

Talbi, El-Ghazali

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

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.