eprintid: 748
rev_number: 8
eprint_status: archive
userid: 17
dir: disk0/00/00/07/48
datestamp: 2011-08-01 10:24:49
lastmod: 2011-08-04 07:30:21
status_changed: 2011-08-01 10:24:49
type: book_section
metadata_visibility: show
item_issues_count: 0
creators_name: Dellino, Gabriella
creators_name: Fedele, Mariagrazia
creators_name: Meloni, Carlo
creators_id: gabriella.dellino@imtlucca.it
creators_id: 
creators_id: 
title: Dynamic objectives aggregation methods in multi-objective evolutionary optimization 
ispublished: pub
subjects: QA
divisions: EIC
full_text_status: none
abstract: Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This chapter proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. Since the incorporation of chaotic rules or behaviour in population-based optimization algorithms has been shown to possibly enhance their searching ability, this study proposes to introduce and evaluate also some chaotic rules in the dynamic weights generation process. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature. 
date: 2011
date_type: published
series: Studies in Computational Intelligence
number: 357
publisher: Springer-Verlag
pagerange: 85-103
id_number: 10.1007/978-3-642-20958-1_6
refereed: TRUE
isbn: 978-3-642-20957-4
book_title: Innovative computing methods and their applications to engineering problems
editors_name: Nedjah, Nadia
editors_name: Dos Santos Coelho, Leandro
editors_name: Mariani, Viviana
editors_name: De Macedo Mourelle, Luiza
official_url: http://dx.doi.org/10.1007/978-3-642-20958-1_6
citation:   Dellino, Gabriella and Fedele, Mariagrazia and Meloni, Carlo  Dynamic objectives aggregation methods in multi-objective evolutionary optimization.   In:  Innovative computing methods and their applications to engineering problems.   Studies in Computational Intelligence  (357).  Springer-Verlag, pp. 85-103.  ISBN 978-3-642-20957-4      (2011)