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Adding a scout-PSO option #893

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Adding a scout-PSO option #893

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f1d8544
Adding a scout-PSO option
lrnv Dec 18, 2020
25d35ba
mutliprocess PSO
lrnv Dec 28, 2020
5bd03ea
Merge branch 'JuliaNLSolvers:master' into master
lrnv May 18, 2021
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49 changes: 34 additions & 15 deletions src/multivariate/solvers/zeroth_order/particle_swarm.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,8 @@ mutable struct ParticleSwarmState{Tx,T} <: ZerothOrderState
x_learn
current_state
iterations::Int
scout_limit::Int
scout_counts::Vector{Int}
end

function initial_state(method::ParticleSwarm, options, d, initial_x::AbstractArray{T}) where T
Expand Down Expand Up @@ -116,7 +118,6 @@ function initial_state(method::ParticleSwarm, options, d, initial_x::AbstractArr
current_state = 0

value!!(d, initial_x)
score[1] = value(d)

# if search space is limited, spread the initial population
# uniformly over the whole search space
Expand Down Expand Up @@ -150,11 +151,9 @@ function initial_state(method::ParticleSwarm, options, d, initial_x::AbstractArr
X[j, 1] = initial_x[j]
X_best[j, 1] = initial_x[j]
end

for i in 2:n_particles
score[i] = value(d, X[:, i])
end

scout_counts = zeros(Int,n_particles)
# According to eq. 9 in "A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems."
scout_limit = Int(ceil(n*n_particles/12))
ParticleSwarmState(
x,
0,
Expand All @@ -172,11 +171,17 @@ function initial_state(method::ParticleSwarm, options, d, initial_x::AbstractArr
best_score,
x_learn,
0,
options.iterations)
options.iterations,
scout_limit,
scout_counts)
end

function update_state!(f, state::ParticleSwarmState{T}, method::ParticleSwarm) where T
n = length(state.x)
if state.limit_search_space
limit_X!(state.X, state.lower, state.upper, state.n_particles, n)
end
compute_cost!(f, state.n_particles, state.X, state.score)

if state.iteration == 0
copyto!(state.best_score, state.score)
Expand All @@ -188,7 +193,8 @@ function update_state!(f, state::ParticleSwarmState{T}, method::ParticleSwarm) w
state.X_best,
state.x,
value(f),
state.n_particles)
state.n_particles,
state.scout_counts) # adding One to the scout count if the personal best did not change.
# Elitist Learning:
# find a new solution named 'x_learn' which is the current best
# solution with one randomly picked variable being modified.
Expand Down Expand Up @@ -237,15 +243,25 @@ function update_state!(f, state::ParticleSwarmState{T}, method::ParticleSwarm) w
state.w, state.c1, state.c2 = update_swarm_params!(state.c1, state.c2, state.w, state.current_state, _f)
update_swarm!(state.X, state.X_best, state.x, n, state.n_particles, state.V, state.w, state.c1, state.c2)

if state.limit_search_space
limit_X!(state.X, state.lower, state.upper, state.n_particles, n)
end
compute_cost!(f, state.n_particles, state.X, state.score)

scout_phase!(state.X, state.X_best, state.scout_counts,state.scout_limit, n,state.n_particles, state.lower)
state.iteration += 1
false
end

function scout_phase!(X::AbstractArray{Tx}, X_best, scout_count, scout_limit, n,n_particles, x0) where Tx

for i in 1:n_particles
if scout_count[i] >= scout_limit
print("Particle $i Regenerated!\n")
for j in 1:n
X[j,i] = x0[j] + 2*(rand()-1/2)*x0[j]
X_best[j,i] = X[j,i]
end
scout_count[i] = 0
end
end
end


function update_swarm!(X::AbstractArray{Tx}, X_best, best_point, n, n_particles, V,
w, c1, c2) where Tx
Expand Down Expand Up @@ -438,10 +454,11 @@ function update_swarm_params!(c1, c2, w, current_state, f::T) where T
end

function housekeeping!(score, best_score, X, X_best, best_point,
F, n_particles)
F, n_particles,scout_counts)
n = size(X, 1)
for i in 1:n_particles
if score[i] <= best_score[i]
scout_counts[i] = 0
best_score[i] = score[i]
for k in 1:n
X_best[k, i] = X[k, i]
Expand All @@ -452,6 +469,8 @@ function housekeeping!(score, best_score, X, X_best, best_point,
end
F = score[i]
end
else
scout_counts[i] += 1
end
end
return F
Expand All @@ -476,7 +495,7 @@ function compute_cost!(f,
X::Matrix,
score::Vector)

for i in 1:n_particles
Threads.@threads for i in 1:n_particles
score[i] = value(f, X[:, i])
end
nothing
Expand Down