Particle swarm optimization: Difference between revisions
Added Kotlin
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GlobalBestPosition: -0.5134004259016365,-1.5512442672625184
GlobalBestValue: -1.9114053788600853</lang>
=={{header|Kotlin}}==
{{trans|JavaScript}}
<lang scala>// version 1.1.51
import java.util.Random
typealias Func = (DoubleArray) -> Double
class Parameters(val omega: Double, val phip: Double, val phig: Double)
class State(
val iter: Int,
val gbpos: DoubleArray,
val gbval: Double,
val min: DoubleArray,
val max: DoubleArray,
val parameters: Parameters,
val pos: Array<DoubleArray>,
val vel: Array<DoubleArray>,
val bpos: Array<DoubleArray>,
val bval: DoubleArray,
val nParticles: Int,
val nDims: Int
) {
fun report(testfunc: String) {
println("Test Function : $testfunc")
println("Iterations : $iter")
println("Global Best Position : ${gbpos.asList()}")
println("Global Best Value : $gbval")
}
}
fun psoInit(
min: DoubleArray,
max: DoubleArray,
parameters: Parameters,
nParticles: Int
): State {
val nDims = min.size
val pos = Array(nParticles) { min }
val vel = Array(nParticles) { DoubleArray(nDims) }
val bpos = Array(nParticles) { min }
val bval = DoubleArray(nParticles) { Double.POSITIVE_INFINITY}
val iter = 0
val gbpos = DoubleArray(nDims) { Double.POSITIVE_INFINITY }
val gbval = Double.POSITIVE_INFINITY
return State(iter, gbpos, gbval, min, max, parameters,
pos, vel, bpos, bval, nParticles, nDims)
}
val r = Random()
fun pso(fn: Func, y: State): State {
val p = y.parameters
val v = DoubleArray(y.nParticles)
val bpos = Array(y.nParticles) { y.min }
val bval = DoubleArray(y.nParticles)
var gbpos = DoubleArray(y.nDims)
var gbval = Double.POSITIVE_INFINITY
for (j in 0 until y.nParticles) {
// evaluate
v[j] = fn(y.pos[j])
// update
if (v[j] < y.bval[j]) {
bpos[j] = y.pos[j]
bval[j] = v[j]
}
else {
bpos[j] = y.bpos[j]
bval[j] = y.bval[j]
}
if (bval[j] < gbval) {
gbval = bval[j]
gbpos = bpos[j]
}
}
val rg = r.nextDouble()
val pos = Array(y.nParticles) { DoubleArray(y.nDims) }
val vel = Array(y.nParticles) { DoubleArray(y.nDims) }
for (j in 0 until y.nParticles) {
// migrate
val rp = r.nextDouble()
var ok = true
vel[j].fill(0.0)
pos[j].fill(0.0)
for (k in 0 until y.nDims) {
vel[j][k] = p.omega * y.vel[j][k] +
p.phip * rp * (bpos[j][k] - y.pos[j][k]) +
p.phig * rg * (gbpos[k] - y.pos[j][k])
pos[j][k] = y.pos[j][k] + vel[j][k]
ok = ok && y.min[k] < pos[j][k] && y.max[k] > pos[j][k]
}
if (!ok) {
for (k in 0 until y.nDims) {
pos[j][k]= y.min[k] + (y.max[k] - y.min[k]) * r.nextDouble()
}
}
}
val iter = 1 + y.iter
return State(
iter, gbpos, gbval, y.min, y.max, y.parameters,
pos, vel, bpos, bval, y.nParticles, y.nDims
)
}
fun iterate(fn: Func, n: Int, y: State): State {
var r = y
var old = y
if (n == Int.MAX_VALUE) {
while (true) {
r = pso(fn, r)
if (r == old) break
}
}
else {
repeat(n) { r = pso(fn, r) }
}
return r
}
fun mccormick(x: DoubleArray): Double {
val (a, b) = x
return Math.sin(a + b) + (a - b) * (a - b) + 1.0 + 2.5 * b - 1.5 * a
}
fun michalewicz(x: DoubleArray): Double {
val m = 10
val d = x.size
var sum = 0.0
for (i in 1..d) {
val j = x[i - 1]
val k = Math.sin(i * j * j / Math.PI)
sum += Math.sin(j) * Math.pow(k, 2.0 * m)
}
return -sum
}
fun main(args: Array<String>) {
var state = psoInit(
min = doubleArrayOf(-1.5, -3.0),
max = doubleArrayOf(4.0, 4.0),
parameters = Parameters(0.0, 0.6, 0.3),
nParticles = 100
)
state = iterate(::mccormick, 40, state)
state.report("McCormick")
println("f(-.54719, -1.54719) : ${mccormick(doubleArrayOf(-.54719, -1.54719))}")
println()
state = psoInit(
min = doubleArrayOf(0.0, 0.0),
max = doubleArrayOf(Math.PI, Math.PI),
parameters = Parameters(0.3, 0.3, 0.3),
nParticles = 1000
)
state = iterate(::michalewicz, 30, state)
state.report("Michalewicz (2D)")
println("f(2.20, 1.57) : ${michalewicz(doubleArrayOf(2.2, 1.57))}")
}</lang>
Sample output:
<pre>
Test Function : McCormick
Iterations : 40
Global Best Position : [-0.5471015946082899, -1.5471991634200966]
Global Best Value : -1.913222941607108
f(-.54719, -1.54719) : -1.913222954882274
Test Function : Michalewicz (2D)
Iterations : 30
Global Best Position : [2.202908690715102, 1.5707970450218895]
Global Best Value : -1.8013034099142804
f(2.20, 1.57) : -1.801140718473825
</pre>
=={{header|ooRexx}}==
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