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index.html

@@ -14723,6 +14723,28 @@ <h2>References</h2>
  <em>Swarm Intelligence</em>, 2023.<br />
 [&nbsp;<a href="index_bib.html#NebLopGarCoe2023automopso">bib</a>&nbsp;| 
 <a href="https://doi.org/10.1007/s11721-023-00227-2">DOI</a>&nbsp;]
+<blockquote>
+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.
+</blockquote>
 
 </dd>
 
@@ -32297,7 +32319,7 @@ <h2>References</h2>
  <em>Operational Optimisation of Water Distribution Networks</em>.
  PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009.<br />
 [&nbsp;<a href="index_bib.html#LopezIbanezPhD">bib</a>&nbsp;| 
-<a href="http://researchrepository.napier.ac.uk/id/eprint/3044">http</a>&nbsp;]
+<a href="https://researchrepository.napier.ac.uk/id/eprint/3044">http</a>&nbsp;]
 
 </dd>
 

index_bib.html

@@ -43,12 +43,12 @@ <h2>What is this?</h2>
 
 <h2>References</h2>
 
-<h1>tmpK9ym6mRPgE.bib</h1><pre>
+<h1>tmpACAZOsiI74.bib</h1><pre>
 @comment{{This file has been generated by bib2bib 1.99}}
 </pre>
 
 <pre>
-@comment{{Command line: bib2bib --warn-error --expand --expand-xrefs authors.bib abbrev.bib journals.bib articles.bib biblio.bib crossref.bib --remove pdf -ob /tmp/tmpK9ym6mRPgE.bib -oc /tmp/citefileM4fVJOVTwQ}}
+@comment{{Command line: bib2bib --warn-error --expand --expand-xrefs authors.bib abbrev.bib journals.bib articles.bib biblio.bib crossref.bib --remove pdf -ob /tmp/tmpACAZOsiI74.bib -oc /tmp/citefilekWW3tK9Yua}}
 </pre>
 
 <pre>
@@ -14937,7 +14937,27 @@ <h1>tmpK9ym6mRPgE.bib</h1><pre>
                   optimizers: experimentation and analysis},
   journal = {Swarm Intelligence},
   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.}
 }
 </pre>
 
@@ -33501,7 +33521,7 @@ <h1>tmpK9ym6mRPgE.bib</h1><pre>
   school = {School of Engineering and the Built Environment},
   year = 2009,
   address = {Edinburgh Napier University, UK},
-  url = {<a href="http://researchrepository.napier.ac.uk/id/eprint/3044">http://researchrepository.napier.ac.uk/id/eprint/3044</a>}
+  url = {<a href="https://researchrepository.napier.ac.uk/id/eprint/3044">https://researchrepository.napier.ac.uk/id/eprint/3044</a>}
 }
 </pre>