metaheuristicOpt (version 2.0.0)

ALO: Optimization using Ant Lion Optimizer

Description

This is the internal function that implements Ant Lion Optimizer Algorithm. It is used to solve continuous optimization tasks. Users do not need to call it directly, but just use metaOpt.

Usage

ALO(FUN, optimType = "MIN", numVar, numPopulation = 40,
  maxIter = 500, rangeVar)

Arguments

FUN

an objective function or cost function,

optimType

a string value that represent the type of optimization. There are two option for this arguments: "MIN" and "MAX". The default value is "MIN", which the function will do minimization. Otherwise, you can use "MAX" for maximization problem. The default value is "MIN".

numVar

a positive integer to determine the number variables.

numPopulation

a positive integer to determine the number populations. The default value is 40.

maxIter

a positive integer to determine the maximum number of iterations. The default value is 500.

rangeVar

a matrix (\(2 \times n\)) containing the range of variables, where \(n\) is the number of variables, and first and second rows are the lower bound (minimum) and upper bound (maximum) values, respectively. If all variable have equal upper bound, you can define rangeVar as matrix (\(2 \times 1\)).

Value

Vector [v1, v2, ..., vn] where n is number variable and vn is value of n-th variable.

Details

This algorithm was proposed by (Mirjalili, 2015). The Ant Lion Optimizer (ALO) algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented.

In order to find the optimal solution, the algorithm follow the following steps.

• Initialization: Initialize the first population of ants and antlions randomly, calculate the fitness of ants and antlions and find the best antlions as the elite (determined optimum).

• Update Ants Position: Select an antlion using Roulette Whell then update ants position based on random walk around selected antlion and elite. Furthermore, calculate the fitness of all ants.

• Replace an antlion with its corresponding ant, if it becomes fitter

• Update elite if an antlion becomes fitter than the elite

• Check termination criteria, if termination criterion is satisfied, return the elite as the optimal solution for given problem. Otherwise, back to Update Ants Position steps.

References

Seyedali Mirjalili, The Ant Lion Optimizer, Advances in Engineering Software, Volume 83, 2015, Pages 80-98, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2015.01.010

See Also

metaOpt

Examples

# NOT RUN {
##################################
## Optimizing the schewefel's problem 2.22 function

# define schewefel's problem 2.22 function as objective function
schewefels2.22 <- function(x){
   return(sum(abs(x)+prod(abs(x))))
}

## Define parameter
numVar <- 5
rangeVar <- matrix(c(-10,10), nrow=2)

## calculate the optimum solution using Ant Lion Optimizer
resultALO <- ALO(schewefels2.22, optimType="MIN", numVar, numPopulation=20,
                 maxIter=100, rangeVar)

## calculate the optimum value using schewefel's problem 2.22 function
optimum.value <- schewefels2.22(resultALO)

# }