Fix some typos

docs/src/algorithms/combinatorial.md

@@ -19,7 +19,7 @@ GRASP
 ```
 
 ```@docs
-Metaheuristics.GreedyRandomizedContructor
+Metaheuristics.GreedyRandomizedConstructor
 ```
 
 ```@docs

src/algorithms/combinatorial/GRASP/GRASP.jl

@@ -23,9 +23,9 @@ Greedy Randomized Adaptive Search Procedure.
 
 - `initial`: an initial solution if necessary.
 - `constructor` parameters for the greedy constructor.
-- `local_search` the local search strategy `BestImprovingSearch()` (default) and `FirstImprovingSearch()`.
+- `local_search` the local search strategy `BestImproveSearch()` (default) and `FirstImproveSearch()`.
 
-See [`GreedyRandomizedContructor`](@ref)
+See [`GreedyRandomizedConstructor`](@ref)
 
 # Example: Knapsack Problem
 
@@ -59,8 +59,8 @@ function main()
     candidates = rand(search_space)
 
     # define each GRASP component
-    constructor  = MH.GreedyRandomizedContructor(;candidates, instance, α = 0.95)
-    local_search = MH.BestImprovingSearch()
+    constructor  = MH.GreedyRandomizedConstructor(;candidates, instance, α = 0.95)
+    local_search = MH.BestImproveSearch()
     neighborhood = MH.TwoOptNeighborhood()
     grasp = MH.GRASP(;constructor, local_search)
 
@@ -76,7 +76,7 @@ end
 main()
 ```
 """
-function GRASP(;initial=nothing, constructor=nothing, local_search=BestImprovingSearch(),
+function GRASP(;initial=nothing, constructor=nothing, local_search=BestImproveSearch(),
                 options = Options(), information=Information())
 	# TODO
 	if isnothing(constructor)

src/algorithms/combinatorial/GRASP/constructor.jl

@@ -1,5 +1,5 @@
 """
-    GreedyRandomizedContructor(;candidates, instance, α, rng)
+    GreedyRandomizedConstructor(;candidates, instance, α, rng)
 
 This structure can be used to use the Greedy Randomized Contructor.
 
@@ -20,7 +20,7 @@ end
 
 See also [`compute_cost`](@ref) and [`GRASP`](@ref)
 """
-Base.@kwdef struct GreedyRandomizedContructor
+Base.@kwdef struct GreedyRandomizedConstructor
     candidates
     instance = nothing
     α::Float64 = 0.6
@@ -33,7 +33,7 @@ end
 Compute the cost for each candidate in `candidates`, for given `constructor` and
 provided `instance`.
 
-See also [`GreedyRandomizedContructor`](@ref) and [`GRASP`](@ref)
+See also [`GreedyRandomizedConstructor`](@ref) and [`GRASP`](@ref)
 """
 function compute_cost(candidates, constructor, instance)
     @warn "Define compute_cost for\nconstructor=$constructor\ninstance=$instance"
@@ -45,9 +45,9 @@ end
 
 Constructor procedure for GRASP.
 
-See also [`GreedyRandomizedContructor`](@ref), [`compute_cost`](@ref) and [`GRASP`](@ref)
+See also [`GreedyRandomizedConstructor`](@ref), [`compute_cost`](@ref) and [`GRASP`](@ref)
 """
-function construct(constructor::GreedyRandomizedContructor)
+function construct(constructor::GreedyRandomizedConstructor)
     candidates = constructor.candidates |> copy
     α = constructor.α
     # create empty solution S

src/algorithms/combinatorial/LocalSearch/LocalSearchUtils.jl

@@ -1,6 +1,6 @@
 abstract type AbstractLocalSearch end
-struct BestImprovingSearch  <: AbstractLocalSearch end
-struct FirstImprovingSearch <: AbstractLocalSearch end
+struct BestImproveSearch  <: AbstractLocalSearch end
+struct FirstImproveSearch <: AbstractLocalSearch end
 
 include("neighborhood.jl")
 
@@ -10,7 +10,7 @@ function local_search(x, neighbourhood::Neighborhood, ls::AbstractLocalSearch, p
     local_search(x, iter, ls, problem)
 end
 
-function local_search(x, neighbourhood::InternalNeighborhood, ::BestImprovingSearch, problem)
+function local_search(x, neighbourhood::InternalNeighborhood, ::BestImproveSearch, problem)
     best = create_solution(copy(x), problem)
     for xnew in neighbourhood
         sol = create_solution(xnew, problem)
@@ -21,7 +21,7 @@ function local_search(x, neighbourhood::InternalNeighborhood, ::BestImprovingSea
     best
 end
 
-function local_search(x, neighbourhood::InternalNeighborhood, ::FirstImprovingSearch, problem)
+function local_search(x, neighbourhood::InternalNeighborhood, ::FirstImproveSearch, problem)
     initial = create_solution(copy(x), problem) 
     for xnew in neighbourhood
         sol = create_solution(xnew, problem)

src/algorithms/combinatorial/VNS/VND.jl

@@ -16,7 +16,7 @@ Variable Neighborhood Descent.
 
 - `initial`: Use this parameter to provide an initial solution (optional).
 - `neighborhood`: Neighborhood structure.
-- `local_search` the local search strategy `BestImprovingSearch()` (default) and `FirstImprovingSearch()`.
+- `local_search` the local search strategy `BestImproveSearch()` (default) and `FirstImproveSearch()`.
 
 
 # Example: Knapsack Problem
@@ -46,7 +46,7 @@ function main()
 
     # list the neighborhood structures
     neighborhood = [MyKPNeighborhood(1), MyKPNeighborhood(2), MyKPNeighborhood(3)]
-    local_search = MH.BestImprovingSearch()
+    local_search = MH.BestImproveSearch()
     # instantiate VNS
     vnd = MH.VND(;neighborhood, local_search)
 
@@ -57,7 +57,7 @@ end
 main()
 ```
 """
-function VND(;initial = nothing, neighborhood = nothing, local_search = BestImprovingSearch(),
+function VND(;initial = nothing, neighborhood = nothing, local_search = BestImproveSearch(),
         options=Options(), information=Information())
 
     parameters = VND(initial, neighborhood, local_search)

src/algorithms/combinatorial/VNS/VNS.jl

@@ -22,7 +22,7 @@ General Variational Neighborhood Search.
 - `initial`: Use this parameter to provide an initial solution (optional).
 - `neighborhood_shaking`: Neighborhood structure for the shaking step.
 - `neighborhood_local`: Neighborhood structure for the local search.
-- `local_search`: the local search strategy `BestImprovingSearch()` (default) and `FirstImprovingSearch()`.
+- `local_search`: the local search strategy `BestImproveSearch()` (default) and `FirstImproveSearch()`.
 - `neighborhood_change`: The procedure for changing among neighborhood structures  (default `SequentialChange()`).
 
 
@@ -62,7 +62,7 @@ function main()
     # list the neighborhood structures
     neighborhood_shaking = [MyKPNeighborhood(6), MyKPNeighborhood(5), MyKPNeighborhood(4)]
     neighborhood_local = [MyKPNeighborhood(3), MyKPNeighborhood(2), MyKPNeighborhood(1)]
-    local_search = MH.BestImprovingSearch()
+    local_search = MH.BestImproveSearch()
     # instantiate VNS
     vnd = MH.VNS(;neighborhood_shaking, neighborhood_local, local_search, options=MH.Options(verbose=true))
 
@@ -74,7 +74,7 @@ main()
 ```
 """
 function VNS(;initial=nothing,neighborhood_shaking=nothing, neighborhood_local=nothing,
-        local_search=FirstImprovingSearch(), neighborhood_change=SequentialChange(),
+        local_search=FirstImproveSearch(), neighborhood_change=SequentialChange(),
         options=Options(), information=Information())
 
     # TODO

test/combinatorial.jl

@@ -153,7 +153,7 @@ end
         # list the neighborhood structures
         neighborhood_shaking = [MyKPNeighborhood(6), MyKPNeighborhood(5), MyKPNeighborhood(4)]
         neighborhood_local = [MyKPNeighborhood(3), MyKPNeighborhood(2), MyKPNeighborhood(1)]
-        local_search = Metaheuristics.FirstImprovingSearch()
+        local_search = Metaheuristics.FirstImproveSearch()
         # instantiate VNS
         vnd = Metaheuristics.VNS(;neighborhood_shaking, neighborhood_local, local_search, options)
 
@@ -166,8 +166,8 @@ end
         ###########################################
         candidates = rand(search_space)
         instance = KPInstance(profit, weight, capacity)
-        constructor  = Metaheuristics.GreedyRandomizedContructor(;candidates, instance, α = 0.95)
-        local_search = Metaheuristics.BestImprovingSearch()
+        constructor  = Metaheuristics.GreedyRandomizedConstructor(;candidates, instance, α = 0.95)
+        local_search = Metaheuristics.BestImproveSearch()
         neighborhood = Metaheuristics.TwoOptNeighborhood()
         grasp = GRASP(;constructor, local_search)