* articles.bib: Minor formatting changes.

articles.bib

@@ -4884,16 +4884,16 @@ @Article{DiaHanXu2018
 }
 
 @Article{DiaLop2020ejor,
-  author =       Diaz_JE #and# Lopez-Ibanez,
-  title =        "Incorporating Decision-Maker's Preferences into the Automatic
-                  Configuration of Bi-Objective Optimisation Algorithms",
-  journal =      ejor,
-  year =         2021,
-  volume =       289,
-  number =       3,
-  pages =        "1209--1222",
-  doi =          "10.1016/j.ejor.2020.07.059",
-  abstract =     "Automatic configuration (AC) methods are increasingly used to
+  author       = Diaz_JE #and# Lopez-Ibanez,
+  title        = {Incorporating Decision-Maker's Preferences into the Automatic
+                  Configuration of Bi-Objective Optimisation Algorithms},
+  journal      = ejor,
+  year         = 2021,
+  volume       = 289,
+  number       = 3,
+  pages        = {1209--1222},
+  doi          = {10.1016/j.ejor.2020.07.059},
+  abstract     = {Automatic configuration (AC) methods are increasingly used to
                   tune and design optimisation algorithms for problems with
                   multiple objectives. Most AC methods use unary quality
                   indicators, which assign a single scalar value to an
@@ -4921,8 +4921,8 @@ @Article{DiaLop2020ejor
                   benchmark problem. Finally, we apply our approach to
                   re-configuring, according to different DM's preferences, a
                   multi-objective optimiser tackling a real-world production
-                  planning problem arising in the manufacturing industry.",
-  supplement =   {https://doi.org/10.5281/zenodo.3749288}
+                  planning problem arising in the manufacturing industry.},
+  supplement   = {https://doi.org/10.5281/zenodo.3749288}
 }
 
 @Article{DiaMouFigCli2002:ejor,
@@ -11886,42 +11886,44 @@ @Article{LopStu2012swarm
 }
 
 @Article{LopStu2012tec,
-  author =       Lopez-Ibanez #and# Stuetzle,
-  title =        "The Automatic Design of Multi-Objective Ant Colony
-                  Optimization Algorithms",
-  journal =      tec,
-  year =         2012,
-  volume =       16,
-  number =       6,
-  pages =        {861--875},
-  doi = {10.1109/TEVC.2011.2182651},
-  abstract = {
-Multi-objective optimization problems are problems with several,
-typically conflicting criteria for evaluating solutions. Without
-any a priori preference information, the Pareto optimality
-principle establishes a partial order among solutions, and the
-output of the algorithm becomes a set of nondominated solutions
-rather than a single one. Various ant colony optimization (ACO)
-algorithms have been proposed in recent years for solving such
-problems. These multi-objective ACO (MOACO) algorithms exhibit
-different design choices for dealing with the particularities of
-the multi-objective context. This paper proposes a formulation of
-algorithmic components that suffices to describe most MOACO
-algorithms proposed so far. This formulation also shows that
-existing MOACO algorithms often share equivalent design choices
-but they are described in different terms. Moreover, this
-formulation is synthesized into a flexible algorithmic framework,
-from which not only existing MOACO algorithms may be
-instantiated, but also combinations of components that were never
-studied in the literature. In this sense, this paper goes beyond
-proposing a new MOACO algorithm, but it rather introduces a
-family of MOACO algorithms. The flexibility of the proposed MOACO
-framework facilitates the application of automatic algorithm
-configuration techniques. The experimental results presented in
-this paper show that the automatically configured MOACO framework
-outperforms the MOACO algorithms that inspired the framework
-itself. This paper is also among the first to apply automatic
-algorithm configuration techniques to multi-objective algorithms.}
+  author       = Lopez-Ibanez #and# Stuetzle,
+  title        = {The Automatic Design of Multi-Objective Ant Colony
+                  Optimization Algorithms},
+  journal      = tec,
+  year         = 2012,
+  volume       = 16,
+  number       = 6,
+  pages        = {861--875},
+  doi          = {10.1109/TEVC.2011.2182651},
+  abstract     = {Multi-objective optimization problems are problems with
+                  several, typically conflicting criteria for evaluating
+                  solutions. Without any a priori preference information, the
+                  Pareto optimality principle establishes a partial order among
+                  solutions, and the output of the algorithm becomes a set of
+                  nondominated solutions rather than a single one. Various ant
+                  colony optimization (ACO) algorithms have been proposed in
+                  recent years for solving such problems. These multi-objective
+                  ACO (MOACO) algorithms exhibit different design choices for
+                  dealing with the particularities of the multi-objective
+                  context. This paper proposes a formulation of algorithmic
+                  components that suffices to describe most MOACO algorithms
+                  proposed so far. This formulation also shows that existing
+                  MOACO algorithms often share equivalent design choices but
+                  they are described in different terms. Moreover, this
+                  formulation is synthesized into a flexible algorithmic
+                  framework, from which not only existing MOACO algorithms may
+                  be instantiated, but also combinations of components that
+                  were never studied in the literature. In this sense, this
+                  paper goes beyond proposing a new MOACO algorithm, but it
+                  rather introduces a family of MOACO algorithms. The
+                  flexibility of the proposed MOACO framework facilitates the
+                  application of automatic algorithm configuration
+                  techniques. The experimental results presented in this paper
+                  show that the automatically configured MOACO framework
+                  outperforms the MOACO algorithms that inspired the framework
+                  itself. This paper is also among the first to apply automatic
+                  algorithm configuration techniques to multi-objective
+                  algorithms.}
 }
 
 @Article{LopStu2013ejor,