Finding a minimum value for the optimization variables of a cost function.

Description

ICA is a function for optimization by Imperialist Competitive Algorithm.

Usage

1
2
3
ICA(cost, nvar, ncountries = 80, nimp = 10, maxiter = 100,
           lb = rep(-10, nvar), ub = rep(10, nvar), 
           beta = 2, P_revolve = 0.3, zeta = 0.02, ...)

Arguments

cost

A cost function to be minimized. The function accept the parameter values as a numerical vector as its principal argument. Additional arguments may be specified through the ... argument below.

nvar

Number of optimization variables of cost function

ncountries

Number of initial countries

nimp

Number of Initial Imperialists

maxiter

Maximum number of iterations allowed.

lb

Lower limit of the optimization region; a numeric vector of length nvar. Will be recycled if necessary.

ub

Upper limit of the optimization region; a numeric vector of length nvar. Will be recycled if necessary.

beta

Assimilation coefficient.

P_revolve

Revolution is the process in which the socio-political characteristics of a country change suddenly.

zeta

Total Cost of Empire = Cost of Imperialist + Zeta * mean(Cost of All Colonies)

...

Additional arguments, if needed, for the function cost.

Details

To use this code, you should only need to prepare your cost function.

Value

An object of class "ICA", a list with components:

call

The call used.

postion

The vector of components for the position of the minimum value found.

value

The minimum value at the optimal position.

nimp

The remaining number of imperialists at the conclusion of the procedure.

trace

A 1-column matrix of successive minimum values found at each iteration of the major loop.

time

The execution time taken to find the best solution.

Note

The steps can be summarized as the below pseudocode:

0) Define objective function or cost function: f(x, ...), x = (x[1], x[2], ..., x[nvar]) ;

1) Initialization of the algorithm. Generate some random solution in the search space and create initial empires.

2) Assimilation: Colonies move towards imperialist states in different in directions.

3) Revolution: Random changes occur in the characteristics of some countries.

4) Position exchange between a colony and imperialist. A colony with a better position than the imperialist, has the chance to take the control of empire by replacing the existing imperialist.

5) Imperialistic competition: All imperialists compete to take possession of colonies of each other.

6) Eliminate the powerless empires. Weak empires lose their power gradually and they will finally be eliminated.

7) If the stop condition is satisfied, stop, if not go to 2.

8) End

Assimilation: Movement of colonies toward imperialists (Assimilation Policy) Revolution: A sudden change in the socio-political characteristics.

Author(s)

Farimah Houshmand, Farzad Eskandari Ph.D. <Askandari@atu.ac.ir>

Maintainer: Farimah Houshmand <hoshmandcomputer@gmail.com>

References

Atashpaz-Gargari, E. and Lucas, C. (2007). Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, Vol. 7, pp. 4661-4666.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
## --------cost function: f(x,y) = x * sin(4 * x) + 1.1 * y * sin(2 * y)
## --------search region: -10 <= x, y <= 10

cost <- function(x) {
  x[1] * sin(4 * x[1]) + 1.1 * x[2] * sin(2 * x[2])
}

ICAout <- ICA(cost, nvar = 2, ncountries = 80, nimp = 10,
              maxiter = 100, lb = -10, ub = 10, 
              beta = 2, P_revolve = 0.3, zeta = 0.02)

summary(ICAout)     ## same as the print method
coef(ICAout)        ## get the position of the minimum
cost(coef(ICAout))  ## cost at the minimum
plot(ICAout)        ## show the history of the process

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.