From dbdd89f32b0974ab29a1d71733903819a64abac3 Mon Sep 17 00:00:00 2001 From: MLopez-Ibanez <2620021+MLopez-Ibanez@users.noreply.github.com> Date: Sat, 20 Apr 2024 07:07:55 +0200 Subject: [PATCH] * abbrev.bib (palmac-pub): New. * articles (MieEskRui2010nautilus): New. * biblio.bib (FraHam2016bor): New. * crossref.bib (BOR2016): New. --- abbrev.bib | 1 + articles.bib | 68 ++++++++++++++++++++++++++++++++++++++++++---------- biblio.bib | 21 +++++++++++----- crossref.bib | 11 ++++++++- 4 files changed, 82 insertions(+), 19 deletions(-) diff --git a/abbrev.bib b/abbrev.bib index 850cea3..690a4ea 100644 --- a/abbrev.bib +++ b/abbrev.bib @@ -146,6 +146,7 @@ @string{ob @string{openreview = "OpenReview.net"} @string{oup = "Oxford University Press"} @string{oup-n = oup # ", " # add-ny} +@string{palmac-pub = "Palgrave Macmillan"} @string{pergamon = "Pergamon Press, " # add-ny} @string{ph = "Prentice Hall, Englewood Cliffs, NJ"} @string{pm = "Palgrave Macmillan Ltd., Basingstoke, UK"} diff --git a/articles.bib b/articles.bib index 26d5cd6..467efdc 100644 --- a/articles.bib +++ b/articles.bib @@ -12886,6 +12886,50 @@ @Article{Mie2014or doi = "10.1007/s00291-012-0297-0" } +@Article{MieEskRui2010nautilus, + author = Miettinen #and# {Eskelinen, Petri} #and# Ruiz_Francisco #and# + Luque_M, + title = {{NAUTILUS} method: {An} interactive technique in + multiobjective optimization based on the nadir point}, + journal = ejor, + year = 2010, + volume = 206, + number = 2, + pages = {426--434}, + month = oct, + issn = {0377-2217}, + shorttitle = {{NAUTILUS} method}, + doi = {10.1016/j.ejor.2010.02.041}, + abstract = {Most interactive methods developed for solving multiobjective + optimization problems sequentially generate Pareto optimal or + nondominated vectors and the decision maker must always allow + impairment in at least one objective function to get a new + solution. The NAUTILUS method proposed is based on the + assumptions that past experiences affect decision makers' + hopes and that people do not react symmetrically to gains and + losses. Therefore, some decision makers may prefer to start + from the worst possible objective values and to improve every + objective step by step according to their preferences. In + NAUTILUS, starting from the nadir point, a solution is + obtained at each iteration which dominates the previous + one. Although only the last solution will be Pareto optimal, + the decision maker never looses sight of the Pareto optimal + set, and the search is oriented so that (s)he progressively + focusses on the preferred part of the Pareto optimal + set. Each new solution is obtained by minimizing an + achievement scalarizing function including preferences about + desired improvements in objective function values. NAUTILUS + is specially suitable for avoiding undesired anchoring + effects, for example in negotiation support problems, or just + as a means of finding an initial Pareto optimal solution for + any interactive procedure. An illustrative example + demonstrates how this new method iterates.}, + language = {en}, + keywords = {Reference point methods, Interactive methods, Multiple + objective programming, Pareto optimality, Preference + information} +} + @Article{MieMusSte2014nimbus, title = {Interactive multiobjective optimization with {NIMBUS} for decision making under uncertainty}, @@ -15543,13 +15587,19 @@ @Article{RuiMar06 } @Article{RuiSabLuq2015wasfga, - author = Ruiz_AB #and# Saborido_R #and# Luque_M, - title = {A preference-based evolutionary algorithm for multiobjective + author = Ruiz_AB #and# Saborido_R #and# Luque_M, + title = {A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm}, - volume = 62, - doi = {10.1007/s10898-014-0214-y}, - abstract = {When solving multiobjective optimization problems, + journal = jgo, + year = 2015, + volume = 62, + number = 1, + pages = {101--129}, + month = may, + annote = {Proposed WASF-GA}, + doi = {10.1007/s10898-014-0214-y}, + abstract = {When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for @@ -15576,13 +15626,7 @@ @Article{RuiSabLuq2015wasfga other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the - problems.}, - number = 1, - journal = jgo, - month = may, - year = 2015, - pages = {101--129}, - annote = "Proposed WASF-GA" + problems.} } @Article{RuiStu04:ejor, diff --git a/biblio.bib b/biblio.bib index 10fd377..1a07ac5 100644 --- a/biblio.bib +++ b/biblio.bib @@ -3294,13 +3294,13 @@ @InCollection{DebSin2009emo } @InCollection{DebSun2006gecco, - author = Deb #and# {Sundar, J.}, - title = {Reference point based multi-objective optimization using + author = Deb #and# {Sundar, J.}, + title = {Reference point based multi-objective optimization using evolutionary algorithms}, - crossref = "GECCO2006", - pages = {635--642}, - doi = "10.1145/1143997.1144112", - annote = "Proposed R-NSGA-II" + pages = {635--642}, + annote = {Proposed R-NSGA-II}, + crossref = {GECCO2006}, + doi = {10.1145/1143997.1144112} } @InProceedings{DebTewDixDut2007finding, @@ -4654,6 +4654,15 @@ @InProceedings{FraGyoNad2020 crossref = "BNAIC2020" } +@InCollection{FraHam2016bor, + author = {Franco, L Alberto} #and# Hamalainen, + title = {Engaging with Behavioral Operational Research: On Methods, + Actors and Praxis}, + pages = {3--25}, + crossref = {BOR2016}, + doi = {10.1057/978-1-137-53551-1_1} +} + @Book{FraLeiRui2014, title = {Manufacturing Scheduling Systems: An Integrated View on Models, Methods, and Tools}, diff --git a/crossref.bib b/crossref.bib index ad53e4e..e02bb9e 100644 --- a/crossref.bib +++ b/crossref.bib @@ -578,6 +578,16 @@ @Proceedings{BNAIC2020 url = {https://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/bnaic2020proceedings.pdf} } +@Book{BOR2016, + booktitle = {Behavioral Operational Research}, + editor = {Kunc, M. and Malpass, J. and White, L.}, + title = {Behavioral Operational Research Theory, Methodology and + Practice}, + year = 2016, + publisher = palmac-pub, + address = add-london +} + @Book{BarChiPaqPre2010emaoa, title = {Experimental Methods for the Analysis of Optimization Algorithms}, @@ -4703,7 +4713,6 @@ @Book{evoworkshops2004 #and#{E. Marchiori and R. Rothlauf and G. D. Smith and G. Squillero} } - @Proceedings{wae1998, title = {Algorithm Engineering, 2nd International Workshop, {WAE}'92}, year = 1998,