func Test_data3d(tst *testing.T) {
// data
prob := "CF9"
dat := PFdata(prob)
X := utl.DblsGetColumn(0, dat)
Y := utl.DblsGetColumn(1, dat)
Z := utl.DblsGetColumn(2, dat)
// figure
plt.SetForEps(1.0, 400)
plt.Plot3dPoints(X, Y, Z, "s=0.05, color='r', facecolor='r', edgecolor='r', xlbl='$f_1$', ylbl='$f_2$', zlbl='$f_3$'")
plt.AxisRange3d(0, 1, 0, 1, 0, 1)
plt.Camera(10, -135, "")
//plt.Camera(10, 45, "")
plt.SaveD("/tmp/goga", io.Sf("cec09-%s.eps", prob))
// interactive
if false {
r := 0.005
scn := vtk.NewScene()
P := vtk.Spheres{X: X, Y: Y, Z: Z, R: utl.DblVals(len(X), r), Color: []float64{1, 0, 0, 1}}
P.AddTo(scn)
scn.Run()
}
}
func Test_igd01(tst *testing.T) {
//verbose()
chk.PrintTitle("igd. igd metric with star equal to trial => igd=0")
// load star values
prob := "UF1"
fStar, err := io.ReadMatrix(io.Sf("./examples/mulobj-cec09/cec09/pf_data/%s.dat", prob))
if err != nil {
tst.Errorf("cannot read fStar matrix:\n%v", err)
return
}
npts := len(fStar)
// optimiser
var opt Optimiser
opt.Default()
opt.Nsol = npts
opt.Ncpu = 1
opt.FltMin = []float64{0, 0} // used to store fStar
opt.FltMax = []float64{1, 1} // used to store fStar
nf, ng, nh := 2, 0, 0
// generator (store fStar into Flt)
gen := func(sols []*Solution, prms *Parameters) {
for i, sol := range sols {
sol.Flt[0], sol.Flt[1] = fStar[i][0], fStar[i][1]
}
}
// objective function (copy fStar from Flt into Ova)
obj := func(f, g, h, x []float64, ξ []int, cpu int) {
f[0], f[1] = x[0], x[1]
}
// initialise optimiser
opt.Init(gen, nil, obj, nf, ng, nh)
// compute igd
igd := StatIgd(&opt, fStar)
io.Pforan("igd = %v\n", igd)
chk.Scalar(tst, "igd", 1e-15, igd, 0)
// plot
if chk.Verbose {
fmt := &plt.Fmt{C: "red", M: ".", Ms: 1, Ls: "None", L: "solutions"}
fS0 := utl.DblsGetColumn(0, fStar)
fS1 := utl.DblsGetColumn(1, fStar)
io.Pforan("len(fS0) = %v\n", len(fS0))
plt.SetForEps(0.75, 300)
opt.PlotAddOvaOva(0, 1, opt.Solutions, true, fmt)
plt.Plot(fS0, fS1, io.Sf("'b.', ms=2, label='star(%s)', clip_on=0", prob))
plt.Gll("$f_0$", "$f_1$", "")
plt.SaveD("/tmp/goga", "igd01.eps")
}
}
func Test_data2d(tst *testing.T) {
prob := "CF4"
dat := PFdata(prob)
X := utl.DblsGetColumn(0, dat)
Y := utl.DblsGetColumn(1, dat)
plt.SetForEps(1.0, 250)
plt.Plot(X, Y, "'r.'")
plt.Gll("$f_1$", "$f_2$", "")
plt.SaveD("/tmp/goga", io.Sf("cec09-%s.eps", prob))
}
func plot2(opt *goga.Optimiser, onlyFront0 bool) {
// plot reference values
f0 := utl.DblsGetColumn(0, opt.Multi_fStar)
f1 := utl.DblsGetColumn(1, opt.Multi_fStar)
plt.Plot(f0, f1, "'b-', label='reference'")
// plot goga values
fmt := &plt.Fmt{C: "r", M: "o", Ms: 3, L: "goga", Ls: "None"}
opt.PlotAddOvaOva(0, 1, opt.Solutions, true, fmt)
plt.Gll("$f_0$", "$f_1$", "")
plt.SaveD("/tmp/goga", io.Sf("m2_%s.eps", opt.RptName))
}
func Test_flt04(tst *testing.T) {
//verbose()
chk.PrintTitle("flt04. two-bar truss. Pareto-optimal")
// configuration
C := NewConfParams()
C.Nisl = 4
C.Ninds = 24
C.GAtype = "crowd"
C.DiffEvol = true
C.CrowdSize = 3
C.ParetoPhi = 0.05
C.Tf = 100
C.Dtmig = 25
C.RangeFlt = [][]float64{{0.1, 2.25}, {0.5, 2.5}}
C.PopFltGen = PopFltGen
C.CalcDerived()
rnd.Init(C.Seed)
// data
// from Coelho (2007) page 19
ρ := 0.283 // lb/in³
H := 100.0 // in
P := 1e4 // lb
E := 3e7 // lb/in²
σ0 := 2e4 // lb/in²
// functions
TSQ2 := 2.0 * math.Sqrt2
fcn := func(f, g, h []float64, x []float64) {
f[0] = 2.0 * ρ * H * x[1] * math.Sqrt(1.0+x[0]*x[0])
f[1] = P * H * math.Pow(1.0+x[0]*x[0], 1.5) * math.Sqrt(1.0+math.Pow(x[0], 4.0)) / (TSQ2 * E * x[0] * x[0] * x[1])
g[0] = σ0 - P*(1.0+x[0])*math.Sqrt(1.0+x[0]*x[0])/(TSQ2*x[0]*x[1])
g[1] = σ0 - P*(1.0-x[0])*math.Sqrt(1.0+x[0]*x[0])/(TSQ2*x[0]*x[1])
}
// objective value function
C.OvaOor = func(ind *Individual, idIsland, t int, report *bytes.Buffer) {
x := ind.GetFloats()
f := make([]float64, 2)
g := make([]float64, 2)
fcn(f, g, nil, x)
ind.Ovas[0] = f[0]
ind.Ovas[1] = f[1]
ind.Oors[0] = utl.GtePenalty(g[0], 0, 1)
ind.Oors[1] = utl.GtePenalty(g[1], 0, 1)
}
// simple problem
sim := NewSimpleFltProb(fcn, 2, 2, 0, C)
sim.Run(chk.Verbose)
// results
if chk.Verbose {
// reference data
_, dat, _ := io.ReadTable("data/coelho-fig1.6.dat")
// Pareto-front
feasible := sim.Evo.GetFeasible()
ovas, _ := sim.Evo.GetResults(feasible)
ovafront, _ := sim.Evo.GetParetoFront(feasible, ovas, nil)
xova, yova := sim.Evo.GetFrontOvas(0, 1, ovafront)
// plot
plt.SetForEps(0.75, 355)
plt.Plot(dat["f1"], dat["f2"], "'k+',ms=3")
x := utl.DblsGetColumn(0, ovas)
y := utl.DblsGetColumn(1, ovas)
plt.Plot(x, y, "'r.'")
plt.Plot(xova, yova, "'ko',markerfacecolor='none',ms=6")
plt.Gll("$f_1$", "$f_2$", "")
plt.SaveD("/tmp/goga", "test_flt04.eps")
}
}