Merge remote-tracking branch 'origin/main' into main

README.md

@@ -1,6 +1,6 @@
 # PyPop7: a Pure-PYthon open-source library of POPulation-based (evolution / swarm / pattern search) black-box OPtimization
 
-[![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/Evolutionary-Intelligence/pypop/blob/main/LICENSE) [![PyPI for PyPop7](https://img.shields.io/badge/PyPI-pypop7-yellowgreen.svg)](https://pypi.org/project/pypop7/) [![Documentation Status](https://readthedocs.org/projects/pypop/badge/?version=latest)](https://pypop.readthedocs.io/en/latest/?badge=latest) [![Coverage](https://github.com/Evolutionary-Intelligence/pypop/blob/main/coverage-badge.svg)](https://coverage.readthedocs.io/) [![arxiv](https://img.shields.io/badge/arxiv-2212.05652-red)](https://arxiv.org/abs/2212.05652) [![Downloads](https://static.pepy.tech/badge/pypop7)](https://pepy.tech/project/pypop7) [![Downloads](https://static.pepy.tech/badge/pypop7/month)](https://pepy.tech/project/pypop7) [![WeChat-Group](https://img.shields.io/badge/WeChat-green)](docs/logo/WeChatGroupTo20240818.jpg)
+[![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/Evolutionary-Intelligence/pypop/blob/main/LICENSE) [![PyPI for PyPop7](https://img.shields.io/badge/PyPI-pypop7-yellowgreen.svg)](https://pypi.org/project/pypop7/) [![Documentation Status](https://readthedocs.org/projects/pypop/badge/?version=latest)](https://pypop.readthedocs.io/en/latest/?badge=latest) [![Coverage](https://github.com/Evolutionary-Intelligence/pypop/blob/main/coverage-badge.svg)](https://coverage.readthedocs.io/) [![arxiv](https://img.shields.io/badge/arxiv-2212.05652-red)](https://arxiv.org/abs/2212.05652) [![Downloads](https://static.pepy.tech/badge/pypop7)](https://pepy.tech/project/pypop7) [![Downloads](https://static.pepy.tech/badge/pypop7/month)](https://pepy.tech/project/pypop7) [![WeChat-Group](https://img.shields.io/badge/WeChat-green)](docs/logo/WeChatGroupTo20240825.jpg)
 
 ```PyPop7``` is a *Pure-PYthon* open-source library of **POPulation-based OPtimization** for single-objective, real-parameter, black-box problems (*currently actively maintained*). Its goal is to provide a *unified* interface and a set of *elegant* algorithmic implementations (e.g., evolutionary algorithms, swarm-based optimizers, pattern search, etc.) for **Black-Box Optimization (BBO)**, *particularly population-based optimizers*, in order to facilitate research repeatability, benchmarking of BBO, and especially real-world applications.
 
@@ -330,7 +330,7 @@ reported in the (now still actively-updated) paper list called
   * Parsopoulos, K.E. and Vrahatis, M.N., 2002. [Recent approaches to global optimization problems through particle swarm optimization](https://link.springer.com/article/10.1023/A:1016568309421). Natural Computing, 1(2), pp.235-306.
   * Clerc, M. and Kennedy, J., 2002. [The particle swarm-explosion, stability, and convergence in a multidimensional complex space](https://ieeexplore.ieee.org/abstract/document/985692). TEVC, 6(1), pp.58-73.
   * Eberhart, R.C., Shi, Y. and Kennedy, J., 2001. [Swarm intelligence](https://www.elsevier.com/books/swarm-intelligence/eberhart/978-1-55860-595-4). Elsevier.
-  * Shi, Y. and Eberhart, R., 1998, May. [A modified particle swarm optimizer](https://ieeexplore.ieee.org/abstract/document/699146). In IEEE World Congress on Computational Intelligence (pp. 69-73). IEEE.
+  * Shi, Y. and Eberhart, R., 1998, May. [A modified particle swarm optimizer](https://ieeexplore.ieee.org/abstract/document/699146). CEC (pp. 69-73). IEEE.
   * Kennedy, J. and Eberhart, R., 1995, November. [Particle swarm optimization](https://ieeexplore.ieee.org/document/488968). In Proceedings of International Conference on Neural Networks (pp. 1942-1948). IEEE.
   * Eberhart, R. and Kennedy, J., 1995, October. [A new optimizer using particle swarm theory](https://ieeexplore.ieee.org/abstract/document/494215). In Proceedings of International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE.
   * Interest Applications: e.g., [Melis et al., 2024, Nature](https://www.nature.com/articles/s41586-024-07293-4); [Grabner et al., 2023, Nature Communications](https://www.nature.com/articles/s41467-023-38943-2); [Morselli et al., 2023, IEEE-TWC](https://ieeexplore.ieee.org/abstract/document/10127621); [Reddy et al., 2023, IEEE-TC](https://ieeexplore.ieee.org/document/10005787); [Zhang et al., 2022, CVPR](https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_On_Adversarial_Robustness_of_Trajectory_Prediction_for_Autonomous_Vehicles_CVPR_2022_paper.html); [Yang et al., PRL, 2022](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.065701); [Guan et al., 2022, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.186001); [Zhong et al., 2022, CVPR](https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.html); [Singh&Hecke, 2021, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.248002); [Weiel, et al., 2021, Nature Mach. Intell](https://www.nature.com/articles/s42256-021-00366-3); [Wintermantel et al., 2020, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.070503); [Tang et al., 2019, TPAMI](https://ieeexplore.ieee.org/abstract/document/8386667); [Sheng et al., 2019, TPAMI](https://ieeexplore.ieee.org/abstract/document/8502935); [CMS Collaboration, 2019, JHEP](https://www.research-collection.ethz.ch/handle/20.500.11850/331761); [Wang et al., 2019, TVCG](https://ieeexplore.ieee.org/abstract/document/8826012); [Zhang et al., 2018, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.121.255703); [Leditzky et al., 2018, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.121.160501); [Pham et al., 2018, TPAMI](https://ieeexplore.ieee.org/abstract/document/8085141); [Villeneuve et al., 2017, Science](https://www.science.org/doi/10.1126/science.aam8393); [Choi et al., 2017, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.223605); [González-Echevarría, et al., 2017, TCAD](https://ieeexplore.ieee.org/document/7465733); [Zhu et al., 2017, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.106101); [Choi et al., 2017, ICCV](https://openaccess.thecvf.com/content_iccv_2017/html/Choi_Robust_Hand_Pose_ICCV_2017_paper.html); [Pickup et al., 2016, IJCV](https://link.springer.com/article/10.1007/s11263-016-0903-8); [Li et al., 2015, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.105502); [Sharp et al., 2015, CHI](https://dl.acm.org/doi/abs/10.1145/2702123.2702179); [Taneja et al., 2015, TPAMI](https://ieeexplore.ieee.org/abstract/document/7045528); [Zhang et al., 2015, IJCV](https://link.springer.com/article/10.1007/s11263-015-0819-8); [Meyer et al., 2015, ICCV](https://research.nvidia.com/publication/2015-12_robust-model-based-3d-head-pose-estimation); [Tompson et al., 2014, TOG](https://dl.acm.org/doi/abs/10.1145/2629500); [Baca et al., 2013, Cell](https://www.cell.com/cell/fulltext/S0092-8674(13)00343-7); [Li et al., PRL, 2013](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.136403); [Kawakami et al., 2013, IJCV](https://link.springer.com/article/10.1007/s11263-013-0632-1); [Kim et al., 2012, Nature](https://www.nature.com/articles/nature11546); [Rahmat-Samii et al., 2012, PIEEE](https://ieeexplore.ieee.org/document/6204306); [Oikonomidis et al., 2012, CVPR](https://ieeexplore.ieee.org/document/6247885); [Li et al., 2011, TPAMI](https://ieeexplore.ieee.org/abstract/document/5567109); [Zhao et al., 2011, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.107.215502); [Zhu et al., 2011, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.106.145501); [Lv et al., 2011, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.106.015503); [Hentschel&Sanders, 2010, PRL](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.104.063603); [Pontani&Conway, 2010, JGCD](https://arc.aiaa.org/doi/abs/10.2514/1.48475); [Zhang et al., 2008, CVPR](https://ieeexplore.ieee.org/document/4587512); [Liebelt&Schertler, 2007, CVPR](https://ieeexplore.ieee.org/abstract/document/4270192); [Hassan et al., 2005, AIAA](https://arc.aiaa.org/doi/abs/10.2514/6.2005-1897)].
@@ -344,7 +344,7 @@ reported in the (now still actively-updated) paper list called
   * Gomez, F.J. and Miikkulainen, R., 1999, July. [Solving non-Markovian control tasks with neuroevolution](https://www.ijcai.org/Proceedings/99-2/Papers/097.pdf). IJCAI. (pp. 1356-1361).
   * Moriarty, D.E. and Mikkulainen, R., 1996. [Efficient reinforcement learning through symbiotic evolution](https://link.springer.com/article/10.1023/A:1018004120707). Machine Learning, 22(1), pp.11-32.
   * Moriarty, D.E. and Miikkulainen, R., 1995. [Efficient learning from delayed rewards through symbiotic evolution](https://www.sciencedirect.com/science/article/pii/B9781558603776500566). ICML (pp. 396-404). Morgan Kaufmann.
-  * Potter, M.A. and De Jong, K.A., 1994, October. [A cooperative coevolutionary approach to function optimization](https://link.springer.com/chapter/10.1007/3-540-58484-6_269). In International Conference on Parallel Problem Solving from Nature (pp. 249-257). Springer, Berlin, Heidelberg.
+  * Potter, M.A. and De Jong, K.A., 1994, October. [A cooperative coevolutionary approach to function optimization](https://link.springer.com/chapter/10.1007/3-540-58484-6_269). PPSN (pp. 249-257). Springer, Berlin, Heidelberg.
 * Simultaneous Perturbation Stochastic Approximation (SPSA) [ [https://www.jhuapl.edu/SPSA/](https://www.jhuapl.edu/SPSA/) ]
   * Spall, J.C., 2005. Introduction to stochastic search and optimization: Estimation, simulation, and control. John Wiley & Sons.
 * Simulated Annealing (SA)

docs/sphinx/source/benchmarks.rst

@@ -6,7 +6,7 @@ which have been commonly used in the black-box optimization / zeroth-order optim
 gradient-free optimization / derivative-free optimization /
 `global optimization <https://link.springer.com/journal/10898>`_ /
 `direct search <https://dl.acm.org/journal/jacm>`_ /
-randomized optimization /
+`randomized optimization <https://www.sciencedirect.com/journal/automatica>`_ /
 `meta-heuristics <http://www.scholarpedia.org/article/Metaheuristics>`_ /
 `evolutionary algorithms <https://direct.mit.edu/evco>`_ /
 `swarm intelligence <https://link.springer.com/journal/11721>`_ community.
@@ -648,3 +648,6 @@ skip this section safely.
 .. autofunction:: pypop7.benchmarks.cases.get_y_shubert
 
 .. autofunction:: pypop7.benchmarks.cases.get_y_schaffer
+
+.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop
+   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop

docs/sphinx/source/index.rst

@@ -136,5 +136,5 @@ Python library.
 
 
 
-.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-index
-   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-index
+.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop
+   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop

docs/sphinx/source/installation.rst

@@ -110,5 +110,5 @@ command:
 After you have installed it successfully, we wish that you could enjoy a happy journey on
 **PyPop7** for black-box optimization.
 
-.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-installation
-   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-installation
+.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop
+   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop

docs/sphinx/source/user-guide.rst

@@ -261,5 +261,5 @@ into this open-source Python library, as shown below (just to name a few):
     <https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1098&context=cstech>`_. In Advances in Computers (Vol. 15,
     pp. 65-118). Elsevier.
 
-.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-user-guide
-   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop-user-guide
+.. image:: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop
+   :target: https://visitor-badge.laobi.icu/badge?page_id=Evolutionary-Intelligence.pypop

pypop7/benchmarks/base_functions.py

@@ -487,7 +487,7 @@ def griewank(x):
 
 class Griewank(BaseFunction):
     def __call__(self, x):
-        """
+        """Class of **Griewank** test function.
 
         Parameters
         ----------

pypop7/optimizers/es/cmaes.py

@@ -26,7 +26,8 @@ class CMAES(ES):
        <https://ieeexplore.ieee.org/abstract/document/10464875>`_, `[Jankowski et al., 2024]
        <https://arxiv.org/abs/2404.02795>`_, `[Martin, 2024, Ph.D. Dissertation (Harvard University)]
        <https://dash.harvard.edu/handle/1/37378922>`_, `[Milekovic et al., 2023, Nature Medicine]
-       <https://doi.org/10.1038/s41591-023-02584-1>`_, `[Falk et al., 2023, PNAS]
+       <https://doi.org/10.1038/s41591-023-02584-1>`_, `[Chen et al., 2023, Science Robotics]
+       <https://www.science.org/doi/10.1126/scirobotics.adc9244>`_, `[Falk et al., 2023, PNAS]
        <https://www.pnas.org/doi/abs/10.1073/pnas.2219558120>`_, `[Thamm&Rosenow, 2023, PRL]
        <https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.116202>`_, `[Brea et al., 2023, Nature
        Communications] <https://www.nature.com/articles/s41467-023-38570-x>`_, `[Ghafouri&Biros, 2023]