implement traveling salesman problem solver with using the random key…

… genetic algorithm

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 - Implement Random key genetic algorithm for permutation optimization problems
 - Implement a GA based search for Job-Shop Scheduling problems that can't be solved by Johnson's rule
+- Implement traveling salesman problem solver with using the random key genetic algorithm
 
 
 ### 0.2.2

src/OperationsResearchModels.jl

@@ -21,6 +21,7 @@ include("knapsack.jl")
 include("latex.jl")
 include("randomkeyga.jl")
 include("johnsons.jl")
+include("travelingsalesman.jl")
 
 import .Network
 import .Transportation
@@ -37,6 +38,7 @@ import .Latex
 import .Utility
 import .RandomKeyGA
 import .Johnsons
+import .TravelingSalesman
 
 
 import .Transportation:
@@ -55,6 +57,7 @@ import .Knapsack: KnapsackResult, KnapsackProblem
 import .Latex: latex
 import .Johnsons: JohnsonResult, johnsons, JohnsonException, makespan, johnsons_ga
 import .RandomKeyGA: Chromosome, run_ga
+import .TravelingSalesman: TravelinSalesmenResult, travelingsalesman
 
 export TransportationProblem, TransportationResult, balance, isbalanced, northwestcorner
 export Connection, ShortestPathResult, MaximumFlowResult, nodes
@@ -71,6 +74,7 @@ export Utility
 export latex
 export Chromosome, run_ga
 export JohnsonResult, johnsons, JohnsonException, makespan, johnsons_ga
+export TravelinSalesmenResult, travelingsalesman
 
 export JuMP, HiGHS
 

src/travelingsalesman.jl

@@ -0,0 +1,38 @@
+module TravelingSalesman
+
+import ..RandomKeyGA: run_ga 
+
+export TravelinSalesmenResult
+export travelingsalesman
+
+struct TravelinSalesmenResult
+    route::Vector{Int}
+    cost::Float64
+end
+
+function travelingsalesman(distancematrix::Matrix{TType}; 
+    popsize = 100, ngen = 1000, pcross = 0.8, pmutate = 0.01, nelites = 1):: TravelinSalesmenResult where {TType<:Float64} 
+
+    n, _ = size(distancematrix)
+
+    function costfn(route::Vector{Int})::Float64 
+        cost = 0.0
+        for i in 1:length(route)-1
+            cost += distancematrix[route[i], route[i+1]]
+        end
+        cost += distancematrix[route[end], route[1]]
+        return cost
+    end 
+
+
+    # popsize::Int, chsize::Int, costfn::F, ngen::Int, pcross::Float64, pmutate::Float64, nelites::Int
+    garesult = run_ga(popsize, n, costfn, ngen, pcross, pmutate, nelites)
+
+    best = garesult.chromosomes[1]
+    cost = costfn(sortperm(best.values))
+
+    return TravelinSalesmenResult(sortperm(best.values), cost)
+end 
+
+
+end # end of module TravelingSalesman

test/runtests.jl

@@ -2,7 +2,9 @@ using Test
 using OperationsResearchModels
 using OperationsResearchModels.Simplex
 
+
 include("testrandomkeyga.jl")
+include("testtravelingsalesman.jl")
 include("testjohnsons.jl")
 include("testutility.jl")
 include("testjump.jl")

test/testtravelingsalesman.jl

@@ -0,0 +1,51 @@
+@testset "Traveling Salesman with Random Key GA" verbose = true begin 
+
+    @testset "Points in a rectangle" begin 
+
+        #
+        # * * * * * * 
+        # *         *
+        # * * * * * *
+        pts = Float64[
+            0 0;
+            0 1;
+            0 2;
+            1 2;
+            2 2;
+            3 2;
+            4 2; 
+            5 2;
+            5 1;
+            5 0;
+            4 0;
+            3 0;
+            2 0;
+            1 0;
+        ]
+
+        n = size(pts, 1)
+
+        distmat = zeros(n, n)
+
+        # create the distance matrix 
+        for i in 1:n
+            for j in 1:n
+                distmat[i, j] = sqrt(sum((pts[i, :] .- pts[j, :]).^2))
+            end 
+        end
+
+        bestval = Inf64
+
+        for i in 1:10
+            result = travelingsalesman(distmat, ngen = 1000, popsize = 100, pcross = 1.0, pmutate = 0.10)
+            if result.cost < bestval
+                bestval = result.cost
+                bestresult = result
+            end
+        end 
+
+        @test bestval == 14.0
+    end 
+
+
+end # end of whole @testset