all tests passed

Author: PasoStudio73

src/strategies/adaptive.jl

@@ -48,7 +48,7 @@ function AdaptiveParticleSwarm(;
     prob_shift=0.25,
     rng::R=Random.GLOBAL_RNG
 ) where {R}
-    swarm = AdaptiveParticleSwarm{R}(n_particles, c1, c2, prob_shift, rng)
+    swarm = AdaptiveParticleSwarm(n_particles, c1, c2, prob_shift, rng)
     message = MLJTuning.clean!(swarm)
     isempty(message) || @warn message
     return swarm

src/strategies/basic.jl

@@ -112,7 +112,7 @@ function ParticleSwarm(;
     prob_shift=0.25,
     rng::R=Random.GLOBAL_RNG
 ) where {R}
-    swarm = ParticleSwarm{R}(n_particles, w, c1, c2, prob_shift, rng)
+    swarm = ParticleSwarm(n_particles, w, c1, c2, prob_shift, rng)
     message = MLJTuning.clean!(swarm)
     isempty(message) || @warn message
     return swarm

test/runtests.jl

@@ -1,13 +1,11 @@
-using Distributed
-addprocs(2)
-
 @everywhere begin
     using MLJParticleSwarmOptimization
     using Random
     using Test
     using ComputationalResources
     using Distributions
     using EvoTrees
+    using MLJ
     using MLJBase
     using MLJTuning
     using StableRNGs

test/strategies/basic.jl

@@ -32,7 +32,7 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
             tuning=RandomSearch(rng=StableRNG(1234)),
             resampling=CV(nfolds=5, rng=StableRNG(8888)),
             range=[r1, r2],
-            measure=mae,
+            measure=MLJ.mae,
             n=15,
             acceleration=acceleration
         )