From 93a52e05f0584bac1fec763f47315c888e706c1b Mon Sep 17 00:00:00 2001
From: MLopez-Ibanez <2620021+MLopez-Ibanez@users.noreply.github.com>
Date: Tue, 9 Jul 2024 16:56:22 +0100
Subject: [PATCH]  * articles.bib (LiLopYao2023archiving): Update details.

---
 articles.bib | 85 +++++++++++++++++++++++++++-------------------------
 1 file changed, 44 insertions(+), 41 deletions(-)

diff --git a/articles.bib b/articles.bib
index 2164801..2ab99a1 100644
--- a/articles.bib
+++ b/articles.bib
@@ -10997,6 +10997,50 @@ @Article{LiGroYanLiu2018multi
   annote =       "highly degenerate Pareto fronts"
 }
 
+@Article{LiLopYao2023archiving,
+  author       = Li_Miqing #and# Lopez-Ibanez #and# Yao_Xin,
+  title        = {Multi-Objective Archiving},
+  journal      = tec,
+  year         = 2023,
+  volume       = 28,
+  number       = 3,
+  pages        = {696--717},
+  doi          = {10.1109/TEVC.2023.3314152},
+  abstract     = {Most multi-objective optimisation algorithms maintain an
+                  archive explicitly or implicitly during their search. Such an
+                  archive can be solely used to store high-quality solutions
+                  presented to the decision maker, but in many cases may
+                  participate in the search process (e.g., as the population in
+                  evolutionary computation). Over the last two decades,
+                  archiving, the process of comparing new solutions with
+                  previous ones and deciding how to update the
+                  archive/population, stands as an important issue in
+                  evolutionary multi-objective optimisation (EMO). This is
+                  evidenced by constant efforts from the community on
+                  developing various effective archiving methods, ranging from
+                  conventional Pareto-based methods to more recent
+                  indicator-based and decomposition-based ones. However, the
+                  focus of these efforts is on empirical performance comparison
+                  in terms of specific quality indicators; there is lack of
+                  systematic study of archiving methods from a general
+                  theoretical perspective. In this paper, we attempt to conduct
+                  a systematic overview of multi-objective archiving, in the
+                  hope of paving the way to understand archiving algorithms
+                  from a holistic perspective of theory and practice, and more
+                  importantly providing a guidance on how to design
+                  theoretically desirable and practically useful archiving
+                  algorithms. In doing so, we also present that archiving
+                  algorithms based on weakly Pareto compliant indicators (e.g.,
+                  $\epsilon$-indicator), as long as designed properly, can
+                  achieve the same theoretical desirables as archivers based on
+                  Pareto compliant indicators (e.g., hypervolume
+                  indicator). Such desirables include the property
+                  limit-optimal, the limit form of the possible optimal
+                  property that a bounded archiving algorithm can have with
+                  respect to the most general form of superiority between
+                  solution sets.}
+}
+
 @Article{LiShaBah2016traffic,
   author =       {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and
                   Khodaei, Amin},
@@ -11079,47 +11123,6 @@ @Article{LiLiTanYao2015many
   numpages =     35,
 }
 
-@Article{LiLopYao2023archiving,
-  author       = Li_Miqing #and# Lopez-Ibanez #and# Yao_Xin,
-  title        = {Multi-Objective Archiving},
-  journal      = tec,
-  year         = 2023,
-  doi          = {10.1109/TEVC.2023.3314152},
-  abstract     = {Most multi-objective optimisation algorithms maintain an
-                  archive explicitly or implicitly during their search. Such an
-                  archive can be solely used to store high-quality solutions
-                  presented to the decision maker, but in many cases may
-                  participate in the search process (e.g., as the population in
-                  evolutionary computation). Over the last two decades,
-                  archiving, the process of comparing new solutions with
-                  previous ones and deciding how to update the
-                  archive/population, stands as an important issue in
-                  evolutionary multi-objective optimisation (EMO). This is
-                  evidenced by constant efforts from the community on
-                  developing various effective archiving methods, ranging from
-                  conventional Pareto-based methods to more recent
-                  indicator-based and decomposition-based ones. However, the
-                  focus of these efforts is on empirical performance comparison
-                  in terms of specific quality indicators; there is lack of
-                  systematic study of archiving methods from a general
-                  theoretical perspective. In this paper, we attempt to conduct
-                  a systematic overview of multi-objective archiving, in the
-                  hope of paving the way to understand archiving algorithms
-                  from a holistic perspective of theory and practice, and more
-                  importantly providing a guidance on how to design
-                  theoretically desirable and practically useful archiving
-                  algorithms. In doing so, we also present that archiving
-                  algorithms based on weakly Pareto compliant indicators (e.g.,
-                  $\epsilon$-indicator), as long as designed properly, can
-                  achieve the same theoretical desirables as archivers based on
-                  Pareto compliant indicators (e.g., hypervolume
-                  indicator). Such desirables include the property
-                  limit-optimal, the limit form of the possible optimal
-                  property that a bounded archiving algorithm can have with
-                  respect to the most general form of superiority between
-                  solution sets.}
-}
-
 @Article{LiTanLiYao2016stochastic,
   title =        {Stochastic ranking algorithm for many-objective optimization
                   based on multiple indicators},