🔖 v0.6.0

Author: HaaLeo

CHANGELOG.md

@@ -2,7 +2,7 @@
 All notable changes to the "swarmlib" pypi package will be documented in this file.  
 This project follows [semantic versioning](https://semver.org/).
 
-## Unreleased - v0.6.0
+## 2019-01-24 - v0.6.0
 * **Changed** the visualization of the _firefly algorithm_ and the _cuckoo search_. Now they both include velocities.
 * **Changed** the firefly algorithm including its API. Now it replays the same problem if `--continuous` is set.
 * **Changed** the `--continuous` flag. It requires no parameter anymore.

README.md

@@ -33,12 +33,23 @@ To print all available algorithms:
 swarm --help
 ```
 
-## Firefly Algorithm
+## Contribution
+
+If you found a bug or are missing a feature do not hesitate to [file an issue](https://github.com/HaaLeo/swarmlib/issues/new/choose).  
+Pull Requests are welcome!
+
+## Support
+When you like this package make sure to [star the repository](https://github.com/HaaLeo/swarmlib/stargazers). I am always looking for new ideas and feedback.  
+In addition, it is possible to [donate via paypal](https://www.paypal.me/LeoHanisch/3eur).
+
+## Algorithms
+
+### Firefly Algorithm
 
 This repository includes the _firefly algorithm_ like Xin-She Yang introduced in his paper [Firefly Algorithms for Multimodal Optimization](https://link.springer.com/chapter/10.1007%2F978-3-642-04944-6_14) in 2009 (DOI: 10.1007/978-3-642-04944-6_14).  
 The implementation was part of the course [Natural computing for learning and optimisation](https://is.cuni.cz/studium/eng/predmety/index.php?do=predmet&kod=NPFL107) at Charles University Prague in winter 2018/2019.
 
-### Features
+#### Features
 
 Enables to apply the firefly algorithm to one of the provided 2D functions. The algorithm tries to find the global minimum of the selected function.  
 
@@ -54,7 +65,7 @@ To print all available options execute:
 swarm fireflies -h
 ```
 
-### API
+#### API
 
 In addition to the cli you can also use the API:
 
@@ -66,11 +77,11 @@ best_firefly = problem.solve()
 problem.replay()
 ```
 
-## Cuckoo search
+### Cuckoo search
 
 This repository also implements the _cuckoo search_ that was introduced by Xin-She Yang and Suash Deb in their paper [Cuckoo Search via Lévy flights](https://ieeexplore.ieee.org/document/5393690) in 2009 (DOI: 10.1109/NABIC.2009.5393690).  
 
-### Features
+#### Features
 
 Enables to apply cuckoo search to one of the provided 2D functions. The algorithm tries to find the global minimum of the selected function.  
 
@@ -88,7 +99,7 @@ To print all available options execute:
 swarm cuckoos -h
 ```
 
-### API
+#### API
 
 In addition to the cli you can also use the API:
 
@@ -99,11 +110,11 @@ problem = CuckooProblem(function=FUNCTIONS['michalewicz'], nests=14)
 best_nest = problem.solve()
 problem.replay()
 ```
-## Particle Swarm Optimization
+### Particle Swarm Optimization
 
 This repository also implements modified _particle swarm optimization_ that was introduced by Yuhui Shi and Russell C. Eberhart in their paper [A modified particle swarm optimizer](https://ieeexplore.ieee.org/document/699146) in 1998 (DOI: 10.1109/ICEC.1998.699146). Their approach introduces a so called _inertia weight_ w. To get the [original particle swarm optimization](https://ieeexplore.ieee.org/document/488968) algorithm, just set the parameter `--weight=1`.
 
-### Features
+#### Features
 
 Enables to particle swarm optimization to one of the provided 2D functions. The algorithm tries to find the global minimum of the selected function.  
 
@@ -121,7 +132,7 @@ To print all available options execute:
 swarm particles -h
 ```
 
-### API
+#### API
 
 In addition to the cli you can also use the API:
 
@@ -133,12 +144,12 @@ best_particle = problem.solve()
 problem.replay()
 ```
 
-## Ant Colony Optimization
+### Ant Colony Optimization
 
 This repository includes an _ant colony optimization_ algorithm for the traveling salesman problem (TSP) like Marco Dorigo, Mauro Birattari, and Thomas Stuetzle introduced in the [IEEE Computational Intelligence Magazine](https://ieeexplore.ieee.org/document/4129846) in November 2006 (DOI: 10.1109/MCI.2006.329691).  
 The implementation was part of the course [Natural computing for learning and optimisation](https://is.cuni.cz/studium/eng/predmety/index.php?do=predmet&kod=NPFL107) at Charles University Prague in winter 2018/2019.
 
-### Features
+#### Features
 
 Enables to apply the ant colony optimization algorithm to a TSP using a [TSPLIB95](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) file and plots the result.
 
@@ -153,7 +164,7 @@ To print all available options execute:
 swarm ants -h
 ```
 
-### API
+#### API
 
 In addition to the cli you can also use the API:
 
@@ -164,12 +175,3 @@ problem = ACOProblem('/path/to/my/tsp-file.tsp', 10)
 if problem.solve():
     problem.show_result()
 ```
-
-## Contribution
-
-If you found a bug or are missing a feature do not hesitate to [file an issue](https://github.com/HaaLeo/swarmlib/issues/new/choose).  
-Pull Requests are welcome!
-
-## Support
-When you like this package make sure to [star the repository](https://github.com/HaaLeo/swarmlib/stargazers). I am always looking for new ideas and feedback.  
-In addition, it is possible to [donate via paypal](https://www.paypal.me/LeoHanisch/3eur).

swarmlib/_version.py

@@ -3,4 +3,4 @@
 #  Licensed under the BSD 3-Clause License. See LICENSE.txt in the project root for license information.
 # ------------------------------------------------------------------------------------------------------
 
-__version__ = '0.5.0'
+__version__ = '0.6.0'