* biblio.bib (LopezIbanezPhD): Fix url.

* articles.bib (NebLopGarCoe2023automopso): add abstract.
iridia-ulb · Dec 3, 2023 · f2083d7 · f2083d7
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diff --git a/.github/workflows/test-and-deploy.yml b/.github/workflows/test-and-deploy.yml
@@ -22,7 +22,7 @@ jobs:
       - name: 💡 Check out repository code
         uses: actions/checkout@v3
         with:
-          fetch-depth: 0
+          fetch-depth: 1
 
       - name: "💡 List files in the repository."
         run: |

diff --git a/articles.bib b/articles.bib
@@ -13084,7 +13084,27 @@ @Article{NebLopGarCoe2023automopso
                   optimizers: experimentation and analysis},
   journal      = swarm,
   year         = 2023,
-  doi          = {10.1007/s11721-023-00227-2}
+  doi          = {10.1007/s11721-023-00227-2},
+  abstract     = {Research in multi-objective particle swarm optimizers
+                  (MOPSOs) progresses by proposing one new MOPSO at a time. In
+                  spite of the commonalities among different MOPSOs, it is
+                  often unclear which algorithmic components are crucial for
+                  explaining the performance of a particular MOPSO
+                  design. Moreover, it is expected that different designs may
+                  perform best on different problem families and identifying a
+                  best overall MOPSO is a challenging task. We tackle this
+                  challenge here by: (1) proposing AutoMOPSO, a flexible
+                  algorithmic template for designing MOPSOs with a design space
+                  that can instantiate thousands of potential MOPSOs; and (2)
+                  searching for good-performing MOPSO designs given a family of
+                  training problems by means of an automatic configuration tool
+                  (irace). We apply this automatic design methodology to
+                  generate a MOPSO that significantly outperforms two
+                  state-of-the-art MOPSOs on four well-known bi-objective
+                  problem families. We also identify the key design choices and
+                  parameters of the winning MOPSO by means of
+                  ablation. AutoMOPSO is publicly available as part of the
+                  jMetal framework.}
 }
 
 @Article{NebLunAlb2008abyss,

diff --git a/biblio.bib b/biblio.bib
@@ -8162,7 +8162,7 @@ @PhDThesis{LopezIbanezPhD
   school =       {School of Engineering and the Built Environment},
   year =         2009,
   address =      {Edinburgh Napier University, UK},
-  url =          {http://researchrepository.napier.ac.uk/id/eprint/3044},
+  url =          {https://researchrepository.napier.ac.uk/id/eprint/3044},
 }
 
 @InCollection{LosSchSeb2012ppsn,