func patternsolver(fn bench.Func, db *sql.DB) (*pattern.Method, optim.Mesh) {
low, up := fn.Bounds()
max, min := up[0], low[0]
mesh := &optim.InfMesh{StepSize: (max - min) / 1}
p := initialpoint(fn)
mesh.SetOrigin(p.Pos)
return pattern.New(p, pattern.DB(db)), mesh
}
func swarmsolver(fn bench.Func, db *sql.DB) (optim.Method, optim.Mesh) {
low, up := fn.Bounds()
n := 20 + 1*len(low)
return New(
NewPopulationRand(n, low, up),
VmaxBounds(fn.Bounds()),
DB(db),
), &optim.BoxMesh{&optim.InfMesh{}, low, up}
}
func initialpoint(fn bench.Func) *optim.Point {
low, up := fn.Bounds()
max, min := up[0], low[0]
pos := make([]float64, len(low))
for i := range low {
pos[i] = rand.Float64()*(max-min) + min
}
return &optim.Point{pos, math.Inf(1)}
}
func patternsolver(fn bench.Func, db *sql.DB) (optim.Method, optim.Mesh) {
low, up := fn.Bounds()
max, min := up[0], low[0]
pos := make([]float64, len(low))
for i := range pos {
pos[i] = low[i] + (up[i]-low[i])/3
}
m := &optim.BoxMesh{&optim.InfMesh{StepSize: (max - min) / 10}, low, up}
m.SetOrigin(pos)
p := &optim.Point{pos, math.Inf(1)}
return New(p, DB(db)), m
}
func pswarmsolver(fn bench.Func, db *sql.DB, n int) (*pattern.Method, optim.Mesh) {
low, up := fn.Bounds()
max, min := up[0], low[0]
mesh := &optim.InfMesh{StepSize: (max - min) / 9}
p := initialpoint(fn)
mesh.SetOrigin(p.Pos)
return pattern.New(p,
pattern.SearchMethod(swarmsolver(fn, db, n), pattern.Share),
pattern.DB(db),
), mesh
}
func swarmsolver(fn bench.Func, db *sql.DB, n int) optim.Method {
low, up := fn.Bounds()
if n < 0 {
n = 30 + 1*len(low)
if n > maxeval/500 {
n = maxeval / 500
}
}
return swarm.New(
swarm.NewPopulationRand(n, low, up),
swarm.VmaxBounds(fn.Bounds()),
swarm.DB(db),
)
}