项目作者： jonasgrebe
项目描述 ：
Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm
高级语言： HTML
项目地址: git://github.com/jonasgrebe/bees-bats-fireflies.git
Implementation of three nature-inspired search algorithms:
How to use the algorithms?
All three algorithms and their variants share a common interface. Basically, all one needs to do in order to use one of the algorithms for optimization, is invoking the search(objective, objective_fct, T) method. The parameters all algorithms have in common (algorithm-independent parameters) are the following:
| parameter |
description |
domain |
| objective |
minimization or maximization problem |
‘min’ or ‘max’ |
| objective_fct |
python function or lambda to optimize |
f: R^d -> R^1 |
| d |
dimensionality of solution-space |
positive integer |
| n |
size of the population, i.e. related to amount of bees, bats and fireflies |
positive integer |
| range_min |
lower bound of solution-space in all dimensions |
real number |
| range_max |
upper bound of solution-space in all dimensions |
real number |
| T |
number of iterations |
positive integer |
The Bees Algorithm
| parameter |
description |
domain |
| nb |
number of best patches (smaller than or equal to n) |
positive integer |
| ne |
number of elite patches (smaller than or equal to nb |
positive integer |
| nrb |
number of recruited foragers per best patch (greater than 0) |
positive integer |
| nre |
number of recruited foragers per elite patch (greater than nrb) |
positive integer |
Additional parameters for the Improved Bees Algorithm that includes neighborhood shrinking and site abandonment:
| parameter |
description |
domain |
| sf |
factor for shrinking the patch size |
(0, 1] |
| sl |
stagnation limit |
positive integer |
The Bat Algorithm
| parameter |
description |
domain |
| a |
initial loudness of all bats |
positive float |
| r_max |
maximum pulse rate of bats |
positive float |
| alpha |
loudness decreasing factor |
(0, 1] |
| gamma |
pulse rate increasing factor |
(0, 1] |
| f_min |
minimum sampled frequency |
positive float |
| f_max |
maximum sampled frequency |
positive float |
The Firefly Algorithm
| parameter |
description |
domain |
| alpha |
neighbor sphere radius |
positive float |
| beta_max |
maximum attractivneness |
positive float |
| gamma |
attractiveness descreasing factor |
positive float |
Example Notebooks
You can find exemplary applications of these three implemented metaheuristics in the following few notebooks:
- notebook_function_test.ipynb: Simples minimization of benchmarking functions
- notebook_mle_cauchy.ipynb: Maximum Likelihood Estimation for randomly generated cauchy-distributed samples
- notebook_spring_design.ipynb: Spring weight minimization given some constraints
- notebook_visualize.ipynb: Example on how to visualize the algorithms in the two-dimensional case
- notebook_cluster.ipynb: Attempt to cluster iris data set and generated data set with the metaheuristics
