whaleOptimization {FSinR}

Whale Optimization Algorithm (Binary Whale Optimization Algorithm)

Description

Generates a search function based on the whale optimization algorithm. This function is called internally within the searchAlgorithm function. Binary Whale Optimization Algorithm (Kumar and Kumar 2018-Oct-16) is an algorithm that simulates the social behavior of humpback whales. This algorithm employs a binary version of the bubble-net hunting strategy. The algorithm starts with an initial population of individuals, and in each iteration updates the individuals according to several possible actions: Encircling prey, Bubble-net attacking or Search for prey

Usage

whaleOptimization(population = 10, iter = 10, verbose = FALSE)

Arguments

population

The number of whales population

iter

The number of iterations of the algorithm

verbose

Print the partial results in each iteration

Value

Returns a search function that is used to guide the feature selection process.

Author(s)

Francisco Aragón Royón

References

Kumar V, Kumar D (2018-Oct-16). “Binary whale optimization algorithm and its application to unit commitment problem.” Neural Computing and Applications. doi: 10.1007/s00521-018-3796-3.

Examples

## Not run:  

## The direct application of this function is an advanced use that consists of using this 
# function directly and performing a search process in a feature space
## Classification problem

# Generates the filter evaluation function
filter_evaluator <- filterEvaluator('determinationCoefficient')

# Generates the search function with WOA
woa_search <- whaleOptimization()
# Performs the search process directly (parameters: dataset, target variable and evaluator)
woa_search(iris, 'Species', filter_evaluator)

## End(Not run)