jonasgrebe/bees-bats-fireflies

Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm

Nature-inspired Metaheuristics: Bees, Bats, and Fireflies

Implementation of three nature-inspired search algorithms:

• Bees Algorithm [Pham et al., 2005]
• Bat Algorithm [Yang, 2010]
• Firefly Algorithm [Yang, 2008]

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

bees-bats-fireflies's Issues

TypeError: 'numpy.float64' object is not callable

Hi there,

I tried your codes, however got the following errors:

72     def evaluation_count_decorator(self, f, x):
     73         self.evaluation_count += 1
---> 74         return f(x)
     75 
     76 

TypeError: 'numpy.float64' object is not callable

Could you pls guide me on this?

Many thanks,
Thang