X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/a2aa3f0f91a668ee6e799bad0f4de90b7b2be452..063fd4437e9bfbefc2f6ed6c932744bb20514751:/BookGPU/Chapters/chapter9/ch9.tex

diff --git a/BookGPU/Chapters/chapter9/ch9.tex b/BookGPU/Chapters/chapter9/ch9.tex
index 0fe38c4..0294f48 100644
--- a/BookGPU/Chapters/chapter9/ch9.tex
+++ b/BookGPU/Chapters/chapter9/ch9.tex
@@ -99,7 +99,7 @@ solutions. The process is repeated until a stopping criterion is
 satisfied. \emph{Evolutionary algorithms}, \emph{swarm
 optimization}, and \emph{ant colonies} fall into this class.
 
-
+\clearpage
 \section{Parallel models for metaheuristics}\label{ch8:sec:paraMeta}
 Optimization problems, whether real-life or academic, are more
 often NP-hard and CPU time and/or memory consuming. Metaheuristics
@@ -188,7 +188,7 @@ solution-level\index{metaheuristics!solution-level parallelism}
 parallel model is problem-dependent.}
 \end{itemize}
 \clearpage
-\section{Challenges for the design of GPU-based metaheuristics}
+\section[Challenges for the design of GPU-based  metaheuristics]{Challenges for the design of GPU-based\hfill\break  metaheuristics}
 \label{ch8:sec:challenges}
 
 Developing GPU-based parallel
@@ -501,7 +501,7 @@ QAPLIB~\cite{burkard1991qaplib}. Speedups up to $10 \times$ are
 achieved by the GPU implementation compared
 to the same sequential implementation on CPU using SA-matrix.
 
-\subsection[Implementing population-based metaheuristics\hfill\break on GPUs]{Implementing population-based metaheuristics on GPUs}
+\subsection[Implementing population-based metaheuristics on GPUs]{Implementing population-based metaheuristics on GPUs}
 
 State-of-the-art works dealing with the implementation of
 p-metaheuristics on GPUs generally rely on parallel models and