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Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization Read chapter Read first chapter

2018 | OriginalPaper | Chapter

2. Possibilistic Framework for Multi-Objective Optimization Under Uncertainty

Authors : Oumayma Bahri, Nahla Ben Amor, El-Ghazali Talbi

Published in: Recent Developments in Metaheuristics

Publisher: Springer International Publishing

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Abstract

Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.

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previous chapter Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization
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Metadata
Title
Possibilistic Framework for Multi-Objective Optimization Under Uncertainty
Authors
Oumayma Bahri
Nahla Ben Amor
El-Ghazali Talbi
Copyright Year
2018
Book
Recent Developments in Metaheuristics

Print ISBN: 978-3-319-58252-8

Electronic ISBN: 978-3-319-58253-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-58253-5_2

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