tune in hyperparam

Signed-off-by: AdityaPandeyCN <adityapand3y666@gmail.com>

Author: AdityaPandeyCN

benchmarks/PSOGlobalOptimization/pso_global_optimizers.jmd

@@ -27,15 +27,14 @@ using OptimizationMetaheuristics, OptimizationSciPy
 using ParallelParticleSwarms
 using ForwardDiff
 using KernelAbstractions
-using CUDA
 using StaticArrays, LinearAlgebra
 
 const PSOKernel     = ParallelParticleSwarms.ParallelPSOKernel
 const SyncPSOKernel = ParallelParticleSwarms.ParallelSyncPSOKernel
 const SerPSO        = ParallelParticleSwarms.SerialPSO
 const HPso          = ParallelParticleSwarms.HybridPSO
 
-const BACKEND = CUDABackend()
+const BACKEND = CPU()  # use CUDABackend() from CUDA.jl on GPU CI
 ```
 
 ```julia
@@ -99,12 +98,13 @@ function _pso_problem(f::BBOBFunction, D::Int; x0 = nothing)
 end
 
 function pso_solve(opt, f::BBOBFunction, D::Int, maxiters::Int;
-        local_maxiters::Int = 50, x0 = nothing)
+        local_maxiters::Int = 150, x0 = nothing)
     prob = _pso_problem(f, D; x0)
     if opt isa HPso
-        solve(prob, opt; maxiters, local_maxiters, abstol = 1.0f-8, reltol = 1.0f-8)
+        solve(prob, opt; maxiters, local_maxiters,
+            abstol = 1.0f-8, reltol = 1.0f-8, w = 0.65f0, wdamp = 0.99f0)
     else
-        solve(prob, opt; maxiters)
+        solve(prob, opt; maxiters, w = 0.65f0, wdamp = 0.99f0)
     end
 end
 
@@ -116,7 +116,7 @@ function _extract_u(sol, D)
 end
 
 function pso_benchmark(opt, funcs, run_length;
-        Ntrials = 15, dimension = 3, local_maxiters = 50, Δf = 1e-6, CI_quantile = 0.25,
+        Ntrials = 15, dimension = 3, local_maxiters = 150, Δf = 1e-6, CI_quantile = 0.25,
         n_particles = 1)
     Nf = length(funcs); Nr = length(run_length)
     success = zeros(Float64, Nf, Nr)
@@ -161,7 +161,7 @@ function pso_benchmark(opt, funcs, run_length;
 end
 
 function pso_tts(opt, funcs; Ntrials = 15, dimension = 3, Δf = 1e-6,
-        local_maxiters = 50, max_run_length = 100_000)
+        local_maxiters = 150, max_run_length = 100_000)
     all_times = Float64[]
     D = dimension
     for f in funcs
@@ -170,10 +170,10 @@ function pso_tts(opt, funcs; Ntrials = 15, dimension = 3, Δf = 1e-6,
             prob = _pso_problem(f, D; x0)
             t0 = time()
             sol = if opt isa HPso
-                solve(prob, opt; maxiters = max_run_length,
-                      local_maxiters, abstol = 1.0f-8, reltol = 1.0f-8)
+                solve(prob, opt; maxiters = max_run_length, local_maxiters,
+                    abstol = 1.0f-8, reltol = 1.0f-8, w = 0.65f0, wdamp = 0.99f0)
             else
-                solve(prob, opt; maxiters = max_run_length)
+                solve(prob, opt; maxiters = max_run_length, w = 0.65f0, wdamp = 0.99f0)
             end
             elapsed = time() - t0
             fval = _to_f64(sol.objective)
@@ -194,13 +194,14 @@ dimension      = 10
 # Exclude unstable BBOB functions: f4 Buche-Rastrigin, f7 Step-Ellipsoidal (segfault),
 # f10 Ellipsoidal-2 (illegal access).
 test_functions = filter(f -> nameof(f.f) ∉ (:f4, :f7, :f10), BBOB.bbob_suite(Val(dimension)))
-run_length     = round.(Int, 10 .^ LinRange(1, 4, 15))
+run_length     = round.(Int, 10 .^ LinRange(1, 4.5, 18))  # up to ~3e4 iters
 Ntrials        = 15
-num_particles  = 5_000
+num_particles  = 8_000
 
 const SUCCESS_Δf = 1e-6
 
-PSO_KEYS = Set(["SerialPSO", "PSOKernel", "SyncPSOKernel", "HybridPSO_LBFGS"])
+# SyncPSOKernel omitted as a standalone entry: Hybrid embeds it before L-BFGS.
+PSO_KEYS = Set(["SerialPSO", "PSOKernel", "HybridPSO_LBFGS"])
 
 setup = Dict(
     "NelderMead"                       => NelderMead(),
@@ -216,10 +217,12 @@ setup = Dict(
     "OptimizationMetaheuristics.ECA"   => chain(OptimizationMetaheuristics.ECA(), isboxed = true),
     "OptimizationMetaheuristics.DE"    => chain(OptimizationMetaheuristics.DE(), isboxed = true),
     "ScipyDifferentialEvolution"       => chain(ScipyDifferentialEvolution(), isboxed = true),
-    "SerialPSO"        => SerPSO(512),
+    "SerialPSO"        => SerPSO(2048),
     "PSOKernel"        => PSOKernel(num_particles; backend = BACKEND, global_update = true),
-    "SyncPSOKernel"    => SyncPSOKernel(num_particles; backend = BACKEND),
-    "HybridPSO_LBFGS"  => HPso(pso = SyncPSOKernel(num_particles; backend = BACKEND); backend = BACKEND),
+    "HybridPSO_LBFGS"  => HPso(
+        pso = SyncPSOKernel(num_particles; backend = BACKEND),
+        local_opt = ParallelParticleSwarms.LBFGS(threshold = 20),
+        backend = BACKEND),
 )
 
 @memoize run_bench(algo) = algo in PSO_KEYS ?