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Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster Convergence Read chapter Read first chapter

2013 | OriginalPaper | Chapter

A Fast Local Search Approach for Multiobjective Problems

Authors : Laurent Moalic, Alexandre Caminada, Sid Lamrous

Published in: Learning and Intelligent Optimization

Publisher: Springer Berlin Heidelberg

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Abstract

In this article, we present a new local method for multiobjective problems. It is an extension of local search algorithms for the single objective case, with specific mechanisms used to build the Pareto set. The performance of the local search algorithm is illustrated by experimental results based on a real problem with three objectives. The problem is issued from electric car-sharing service with a car manufacturer partner. Compared to the Multiobjective Pareto Local Search (PLS) well known in the scientific literature [1], the proposed model aims to improve: the solutions quality and the time computing.

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Metadata
Title
A Fast Local Search Approach for Multiobjective Problems
Authors
Laurent Moalic
Alexandre Caminada
Sid Lamrous
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-642-44972-7

Electronic ISBN: 978-3-642-44973-4

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-44973-4_32

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