added figure

paper.bib

@@ -8,3 +8,102 @@ @article{pinnau2017consensus
   year={2017},
   publisher={World Scientific}
 }
+@article{mohan2012survey,
+  title={A survey: Ant Colony Optimization based recent research and implementation on several engineering domain},
+  author={Mohan, B Chandra and Baskaran, R},
+  journal={Expert Systems with Applications},
+  volume={39},
+  number={4},
+  pages={4618--4627},
+  year={2012},
+  publisher={Elsevier}
+}
+@article{karaboga2014comprehensive,
+  title={A comprehensive survey: artificial bee colony (ABC) algorithm and applications},
+  author={Karaboga, Dervis and Gorkemli, Beyza and Ozturk, Celal and Karaboga, Nurhan},
+  journal={Artificial intelligence review},
+  volume={42},
+  pages={21--57},
+  year={2014},
+  publisher={Springer}
+}
+@inproceedings{yang2009firefly,
+  title={Firefly algorithms for multimodal optimization},
+  author={Yang, Xin-She},
+  booktitle={International symposium on stochastic algorithms},
+  pages={169--178},
+  year={2009},
+  organization={Springer}
+}
+@article{bayraktar2013wind,
+  title={The wind driven optimization technique and its application in electromagnetics},
+  author={Bayraktar, Zikri and Komurcu, Muge and Bossard, Jeremy A and Werner, Douglas H},
+  journal={IEEE transactions on antennas and propagation},
+  volume={61},
+  number={5},
+  pages={2745--2757},
+  year={2013},
+  publisher={IEEE}
+}
+@inproceedings{kennedy1995particle,
+  title={Particle swarm optimization},
+  author={Kennedy, James and Eberhart, Russell},
+  booktitle={Proceedings of ICNN'95-international conference on neural networks},
+  volume={4},
+  pages={1942--1948},
+  year={1995},
+  organization={IEEE}
+}
+@article{henderson2003theory,
+  title={The theory and practice of simulated annealing},
+  author={Henderson, Darrall and Jacobson, Sheldon H and Johnson, Alan W},
+  journal={Handbook of metaheuristics},
+  pages={287--319},
+  year={2003},
+  publisher={Springer}
+}
+@article{fornasier2021consensus,
+  title={Consensus-based optimization methods converge globally},
+  author={Fornasier, Massimo and Klock, Timo and Riedl, Konstantin},
+  journal={arXiv preprint arXiv:2103.15130},
+  year={2021}
+}
+@incollection{totzeck2021trends,
+  title={Trends in consensus-based optimization},
+  author={Totzeck, Claudia},
+  booktitle={Active Particles, Volume 3: Advances in Theory, Models, and Applications},
+  pages={201--226},
+  year={2021},
+  publisher={Springer}
+}
+@article{carrillo2022consensus,
+  title={Consensus-based sampling},
+  author={Carrillo, Jos{\'e} A and Hoffmann, Franca and Stuart, Andrew M and Vaes, Urbain},
+  journal={Studies in Applied Mathematics},
+  volume={148},
+  number={3},
+  pages={1069--1140},
+  year={2022},
+  publisher={Wiley Online Library}
+}
+@book{ackley2012connectionist,
+  title={A connectionist machine for genetic hillclimbing},
+  author={Ackley, David},
+  volume={28},
+  year={2012},
+  publisher={Springer science \& business media}
+}
+@article{miranda2018pyswarms,
+  title={PySwarms: a research toolkit for Particle Swarm Optimization in Python},
+  author={Miranda, Lester James},
+  journal={Journal of Open Source Software},
+  volume={3},
+  number={21},
+  pages={433},
+  year={2018}
+}
+@software{scikitopt,
+author = {Guo, Fei},
+title = {{scikit-opt}},
+url = {https://github.com/guofei9987/scikit-opt}
+}

paper.md

@@ -19,7 +19,7 @@ bibliography: paper.bib
 
 We present CBXpy and CBX.jl which provide Python and respectively Julia implementations for consensus-based interacting particle methods. In detail, the packages focus on consensus-based optimization (CBO) [@pinnau2017consensus] and consensus-based sampling (CBS) [@carrillo2022consensus], which coined the acronym CBX. The Python and Julia implementations were developed in parallel, in order to provide a framework for researchers more familiar with either language. Here, we focused on having a similar API and core functionality in both packages, while taking advantage of the strengths of each language, and writing idiomatic code.
 
-[JOSS.png]
+![Visualization of a CBO run for the Ackley function [@ackley2012connectionist].](JOSS.png)
 
 # Statement of need
 
@@ -31,6 +31,8 @@ $$
 
 for some input space $\mathcal{X}$ and a possibly non-convex objective function $f:\mathcal{X}\to\mathbb{R}$. As an agent-based method, CBO is conceptually comparable to biologically and physically inspired methods [@mohan2012survey;karaboga2014comprehensive;yang2009firefly;bayraktar2013wind], particle-swarm optimization (PSO) [@kennedy1995particle] or simulated annealing [@henderson2003theory]. However, compared to other heuristics, one can derive a limiting PDE in the infinite-particle limit, which has sparked considerable theoretical interest in recent years [@totzeck2021trends]. From a computational side, the method is also attractive, since the amount of particle interaction scales linearly with the number of particles. 
 
+For PSO and SA there are already available Python implementations [@miranda2018pyswarms;@scikitopt]
+
 
 
 # Acknowledgements