Release new alpha version 2.5.4a6

thieu1995 · Jun 20, 2023 · c525dcd · c525dcd
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diff --git a/ChangeLog.md b/ChangeLog.md
@@ -23,6 +23,12 @@ Different versions of mealpy in terms of passing hyper-parameters. So please car
   + amend_position() from problem. This means for problem level (transform to the correct solution)
 + Fix bugs coefficients in GWO-based optimizers.
 + Fig bug self.epoch in SCSO optimizer.
++ Fix bug self.dyn_pop_size when pop_size is small value
++ Move SHADE-based optimizers from DE to SHADE module in evolutionary_based group
++ Add Improved Grey Wolf Optimization (IGWO) in GWO algorithm
++ Add Tabu Search (TS) to math-based group
++ Add get_all_optimizers() and get_optimizer_by_name() in Mealpy
+
 
 
 # Version 2.5.3

diff --git a/README.md b/README.md
@@ -31,7 +31,7 @@ MEALPY is the largest python library for most of the cutting-edge nature-inspire
 approximate optimization.
 
 * **Free software:** GNU General Public License (GPL) V3 license
-* **Total algorithms**: 174 (102 original, 45 official variants, 27 developed variants)
+* **Total algorithms**: 175 (102 original, 46 official variants, 27 developed variants)
 * **Documentation:** https://mealpy.readthedocs.io/en/latest/
 * **Python versions:** 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x
 * **Dependencies:** numpy, scipy, pandas, matplotlib
@@ -60,7 +60,7 @@ $ pip install mealpy==2.5.3
 
 ### Install the alpha/beta version
 ```sh 
-$ pip install mealpy==2.5.4a5
+$ pip install mealpy==2.5.4a6
 ```
 
 ### Install the pre-release version
@@ -80,6 +80,9 @@ After installation, you can import Mealpy as any other Python module:
 $ python
 >>> import mealpy
 >>> mealpy.__version__
+
+>>> print(mealpy.get_all_optimizers())
+>>> model = mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100, pop_size=50)
 ```
 
 Let's go through a basic and advanced example.
@@ -364,14 +367,14 @@ All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages
 * Issue tracker: https://github.com/thieu1995/mealpy/issues
 * Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md
 * Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md
+* Official chat/support group: https://t.me/+fRVCJGuGJg1mNDg1
 
 * This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here
     * https://github.com/thieu1995/opfunu
     * https://github.com/thieu1995/metaheuristics
     * https://github.com/aiir-team
-
-**Want to have an instant assistant? Join our telegram community at [link](https://t.me/+fRVCJGuGJg1mNDg1)**
-We share lots of information, questions, and answers there. You will get more support and knowledge there.
+    * https://github.com/mafese
+    * https://github.com/permetrics
 
 ### Cite Us
 
@@ -383,7 +386,8 @@ If you are using mealpy in your project, we would appreciate citations:
   author={Van Thieu, Nguyen and Mirjalili, Seyedali},
   journal={Journal of Systems Architecture},
   year={2023},
-  publisher={Elsevier}
+  publisher={Elsevier},
+  doi={10.1016/j.sysarc.2023.102871}
 }
 
 @article{van2023groundwater,
@@ -393,7 +397,8 @@ If you are using mealpy in your project, we would appreciate citations:
   volume={617},
   pages={129034},
   year={2023},
-  publisher={Elsevier}
+  publisher={Elsevier},
+  doi={10.1016/j.jhydrol.2022.129034}
 }
 ```
 

diff --git a/docs/source/pages/general/simple_guide.rst b/docs/source/pages/general/simple_guide.rst
@@ -23,26 +23,25 @@ Installation
 
 **User Installation**
 
-Install the [current PyPI release](https://pypi.python.org/pypi/mealpy):
+Install the `current PyPI release`_. ::
 
-::
+   $ pip install mealpy==2.5.3
 
-   $ pip install mealpy==2.5.0
+.. _current PyPI release: https://pypi.python.org/pypi/mealpy
 
-Or install the development version from GitHub:
-
-::
+Or install the development version from GitHub::
 
    $ pip install git+https://github.com/thieu1995/mealpy
 
 
-Check the version of MEALPY:
-
-::
+Check the version of MEALPY::
 
    $ import mealpy
    $ mealpy.__version__
 
+   $ print(mealpy.get_all_optimizers())
+   $ model = mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100, pop_size=50)
+
 ----------------------
 Getting started in 30s
 ----------------------
@@ -193,4 +192,4 @@ The returned results are 2 values :
    :maxdepth: 4
 
 .. toctree::
-   :maxdepth: 4
+   :maxdepth: 4
diff --git a/docs/source/pages/support.rst b/docs/source/pages/support.rst
@@ -2,39 +2,53 @@
 Cite Us
 =======
 
-If you are using mealpy in your project, we would appreciate citations:
-
-::
-
-   @software{nguyen_van_thieu_2022_6684223,
-      author       = {Nguyen Van Thieu and Seyedali Mirjalili},
-      title        = {{MEALPY: a Framework of The State-of-The-Art Meta-Heuristic Algorithms in Python}},
-      month        = jun,
-      year         = 2022,
-      publisher    = {Zenodo},
-      version      = {v2.5.0},
-      doi          = {10.5281/zenodo.6684223},
-      url          = {https://doi.org/10.5281/zenodo.6684223}
+If you are using mealpy in your project, we would appreciate citations::
+
+   @article{van2023mealpy,
+      title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
+      author={Van Thieu, Nguyen and Mirjalili, Seyedali},
+      journal={Journal of Systems Architecture},
+      year={2023},
+      publisher={Elsevier},
+      doi={10.1016/j.sysarc.2023.102871}
    }
 
+   @article{van2023groundwater,
+      title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
+      author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
+      journal={Journal of Hydrology},
+      volume={617},
+      pages={129034},
+      year={2023},
+      publisher={Elsevier},
+      doi={10.1016/j.jhydrol.2022.129034}
+   }
+```
+
+
 If you have an open-ended or a research question, you can contact me via [email protected]
 
 
 ===============
 Important links
 ===============
 
+
 * Official source code repo: https://github.com/thieu1995/mealpy
 * Official document: https://mealpy.readthedocs.io/
 * Download releases: https://pypi.org/project/mealpy/
 * Issue tracker: https://github.com/thieu1995/mealpy/issues
 * Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md
 * Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md
+* Official chat/support group: https://t.me/+fRVCJGuGJg1mNDg1
 
-* This project also related to my another projects which are "meta-heuristics" and "neural-network", check it here
+* This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here
     * https://github.com/thieu1995/opfunu
     * https://github.com/thieu1995/metaheuristics
     * https://github.com/aiir-team
+    * https://github.com/mafese
+    * https://github.com/permetrics
+
 
 
 ====================

diff --git a/run.py b/run.py
@@ -23,6 +23,8 @@
 
 # from mealpy.utils.problem import Problem
 from mealpy import Problem
+from mealpy import get_all_optimizers, get_optimizer_by_name
+
 
 ndim = 30
 f18 = F292017(ndim, f_bias=0)
@@ -63,61 +65,69 @@ def fit_func(self, solution):
 P1 = Squared(lb=[-10, ] * 100, ub=[10, ] * 100, minmax="min")
 
 if __name__ == "__main__":
-    model = WOA.OriginalWOA(epoch, pop_size)
-    model = OriginalBMO(epoch, pop_size)
-    model = OriginalTPO(epoch, pop_size)
-    model = OriginalEHO(epoch, pop_size)
-    model = OriginalESOA(epoch, pop_size)
-    model = T1.BaseBBO(epoch, pop_size)
-    model = T2.OriginalBBO(epoch, pop_size)
-    model = BaseBBO(epoch, pop_size)
-    model = LARO(epoch, pop_size)
-    model = OriginalARO(epoch, pop_size)
-    model = MGTO(epoch, pop_size)
-    model = EOA.OriginalEOA(epoch, pop_size)
-    model = SBO.OriginalSBO(epoch, pop_size)
-    model = SMA.OriginalSMA(epoch, pop_size)
-    model = SOA.DevSOA(epoch, pop_size)
-    model = MA.OriginalMA(epoch, pop_size)
-    model = BRO.BaseBRO(epoch, pop_size)
-    model = BSO.ImprovedBSO(epoch, pop_size)
-    model = CHIO.BaseCHIO(epoch, pop_size)
-    model = FBIO.OriginalFBIO(epoch, pop_size)
-    model = HBO.OriginalHBO(epoch, pop_size)
-    model = QSA.BaseQSA(epoch, pop_size)
-    model = QSA.OriginalQSA(epoch, pop_size)
-    model = QSA.OppoQSA(epoch, pop_size)
-    model = QSA.ImprovedQSA(epoch, pop_size)
-    model = SARO.BaseSARO(epoch, pop_size)
-    model = SARO.OriginalSARO(epoch, pop_size)
-    model = TLO.BaseTLO(epoch, pop_size)
-    model = TLO.ImprovedTLO(epoch, pop_size)
-    model = TLO.OriginalTLO(epoch, pop_size)
-    model = PSS.OriginalPSS(epoch, pop_size)
-    model = ASO.OriginalASO(epoch, pop_size)
-    model = EO.ModifiedEO(epoch, pop_size)
-    model = EO.AdaptiveEO(epoch, pop_size)
-    model = EO.OriginalEO(epoch, pop_size)
-    model = FLA.OriginalFLA(epoch, pop_size)
-    model = BFO.OriginalBFO(epoch, pop_size)
-    model = BFO.ABFO(epoch, pop_size)
-    model = GJO.OriginalGJO(epoch, pop_size)
-    model = GTO.Matlab102GTO(epoch, pop_size)
-    model = HHO.OriginalHHO(epoch, pop_size)
-    model = MPA.OriginalMPA(epoch, pop_size)
-    model = SeaHO.OriginalSeaHO(epoch, pop_size)
-    model = SRSR.OriginalSRSR(epoch, pop_size)
-    model = AVOA.OriginalAVOA(epoch, pop_size)
-    model = SA.OriginalSA(epoch, pop_size)
-    model = BSO.OriginalBSO(epoch, pop_size)
+    # model = WOA.OriginalWOA(epoch, pop_size)
+    # model = OriginalBMO(epoch, pop_size)
+    # model = OriginalTPO(epoch, pop_size)
+    # model = OriginalEHO(epoch, pop_size)
+    # model = OriginalESOA(epoch, pop_size)
+    # model = T1.BaseBBO(epoch, pop_size)
+    # model = T2.OriginalBBO(epoch, pop_size)
+    # model = BaseBBO(epoch, pop_size)
+    # model = LARO(epoch, pop_size)
+    # model = OriginalARO(epoch, pop_size)
+    # model = MGTO(epoch, pop_size)
+    # model = EOA.OriginalEOA(epoch, pop_size)
+    # model = SBO.OriginalSBO(epoch, pop_size)
+    # model = SMA.OriginalSMA(epoch, pop_size)
+    # model = SOA.DevSOA(epoch, pop_size)
+    # model = MA.OriginalMA(epoch, pop_size)
+    # model = BRO.BaseBRO(epoch, pop_size)
+    # model = BSO.ImprovedBSO(epoch, pop_size)
+    # model = CHIO.BaseCHIO(epoch, pop_size)
+    # model = FBIO.OriginalFBIO(epoch, pop_size)
+    # model = HBO.OriginalHBO(epoch, pop_size)
+    # model = QSA.BaseQSA(epoch, pop_size)
+    # model = QSA.OriginalQSA(epoch, pop_size)
+    # model = QSA.OppoQSA(epoch, pop_size)
+    # model = QSA.ImprovedQSA(epoch, pop_size)
+    # model = SARO.BaseSARO(epoch, pop_size)
+    # model = SARO.OriginalSARO(epoch, pop_size)
+    # model = TLO.BaseTLO(epoch, pop_size)
+    # model = TLO.ImprovedTLO(epoch, pop_size)
+    # model = TLO.OriginalTLO(epoch, pop_size)
+    # model = PSS.OriginalPSS(epoch, pop_size)
+    # model = ASO.OriginalASO(epoch, pop_size)
+    # model = EO.ModifiedEO(epoch, pop_size)
+    # model = EO.AdaptiveEO(epoch, pop_size)
+    # model = EO.OriginalEO(epoch, pop_size)
+    # model = FLA.OriginalFLA(epoch, pop_size)
+    # model = BFO.OriginalBFO(epoch, pop_size)
+    # model = BFO.ABFO(epoch, pop_size)
+    # model = GJO.OriginalGJO(epoch, pop_size)
+    # model = GTO.Matlab102GTO(epoch, pop_size)
+    # model = HHO.OriginalHHO(epoch, pop_size)
+    # model = MPA.OriginalMPA(epoch, pop_size)
+    # model = SeaHO.OriginalSeaHO(epoch, pop_size)
+    # model = SRSR.OriginalSRSR(epoch, pop_size)
+    # model = AVOA.OriginalAVOA(epoch, pop_size)
+    # model = SA.OriginalSA(epoch, pop_size)
+    # model = BSO.OriginalBSO(epoch, pop_size)
     # model = BSO.ImprovedBSO(epoch, pop_size)
-    model = SCSO.OriginalSCSO(epoch, pop_size)
-    model = TS.OriginalTS(epoch, pop_size=2, tabu_size=5, neighbour_size=20, perturbation_scale=0.05)
-    model = GWO.OriginalGWO(epoch, pop_size)
-    model = GWO.GWO_WOA(epoch, pop_size)
-    model = GWO.RW_GWO(epoch, pop_size)
-    model = GWO.IGWO(epoch, pop_size, a_min=0.02, a_max=1.6)
-    best_position, best_fitness = model.solve(P1)#, mode="thread", n_workers=4, termination=term_dict1)
+    # model = SCSO.OriginalSCSO(epoch, pop_size)
+    # model = TS.OriginalTS(epoch, pop_size=2, tabu_size=5, neighbour_size=20, perturbation_scale=0.05)
+    # model = GWO.OriginalGWO(epoch, pop_size)
+    # model = GWO.GWO_WOA(epoch, pop_size)
+    # model = GWO.RW_GWO(epoch, pop_size)
+
+    ## 1st way
+    # model = GWO.IGWO(epoch, pop_size, a_min=0.02, a_max=1.6)
+
+    for opt_name, opt_class in get_all_optimizers().items():
+        print(f"{opt_name}: {opt_class}")
+
+    ## 2nd way
+    model = get_optimizer_by_name("IGWO")(epoch, pop_size, a_min=0.02, a_max=1.6)
+    best_position, best_fitness = model.solve(P1, mode="thread", n_workers=4, termination=term_dict1)
 
     print(best_position)
     print(model.get_parameters())

diff --git a/setup.py b/setup.py
@@ -15,7 +15,7 @@ def readme():
 
 setup(
     name="mealpy",
-    version="2.5.4-alpha.5",
+    version="2.5.4-alpha.6",
     author="Thieu",
     author_email="[email protected]",
     description="MEALPY: An Open-source Library for Latest Meta-heuristic Algorithms in Python",