Simulated annealing: Difference between revisions
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</pre> |
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=={{header|Sidef}}== |
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{{trans|Julia}} |
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<lang ruby>module TravelingSalesman { |
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# Eₛ: length(path) |
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func Eₛ(distances, path) { |
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var total = 0 |
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[path, path.slice(1)].zip {|ci,cj| |
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total += distances[ci-1][cj-1] |
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} |
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total |
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} |
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# T: temperature |
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func T(k, kmax, kT) { kT * (1 - k/kmax) } |
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# ΔE = Eₛ_new - Eₛ_old > 0 |
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# Prob. to move if ΔE > 0, → 0 when T → 0 (fronzen state) |
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func P(ΔE, k, kmax, kT) { exp(-ΔE / T(k, kmax, kT)) } |
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# ∆E from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..) |
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# ∆E before swapping (u,v) |
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# Quicker than Eₛ(s_next) - Eₛ(path) |
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func dE(distances, path, u, v) { |
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var a = distances[path[u-1]-1][path[u]-1] |
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var b = distances[path[u+1]-1][path[u]-1] |
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var c = distances[path[v-1]-1][path[v]-1] |
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var d = distances[path[v+1]-1][path[v]-1] |
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var na = distances[path[u-1]-1][path[v]-1] |
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var nb = distances[path[u+1]-1][path[v]-1] |
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var nc = distances[path[v-1]-1][path[u]-1] |
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var nd = distances[path[v+1]-1][path[u]-1] |
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if (v == u+1) { |
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return ((na+nd) - (a+d)) |
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} |
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if (u == v+1) { |
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return ((nc+nb) - (c+b)) |
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} |
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return ((na+nb+nc+nd) - (a+b+c+d)) |
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} |
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const dirs = [1, -1, 10, -10, 9, 11, -11, -9] |
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func _prettypath(path) { |
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path.slices(10).map { .map{ "%3s" % _ }.join(', ') }.join("\n") |
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} |
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func findpath(distances, kmax, kT) { |
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const n = distances.len |
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const R = 2..n |
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var path = [1, R.shuffle..., 1] |
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var Emin = Eₛ(distances, path) |
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printf("# Entropy(s₀) = s%10.2f\n", Emin) |
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printf("# Random path:\n%s\n\n", _prettypath(path)) |
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for k in (1 .. kmax) { |
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if (k % (kmax//10) == 0) { |
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printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", k, T(k, kmax, kT), Eₛ(distances, path)) |
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} |
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var u = R.rand |
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var v = (path[u-1] + dirs.rand) |
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v ~~ R || next |
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var δE = dE(distances, path, u-1, v-1) |
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if ((δE < 0) || (P(δE, k, kmax, kT) >= 1.rand)) { |
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path.swap(u-1, v-1) |
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Emin += δE |
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} |
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} |
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printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", kmax, T(kmax, kmax, kT), Eₛ(distances, path)) |
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say ("\n# Found path:\n", _prettypath(path)) |
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return path |
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} |
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} |
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var citydist = {|ci| |
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{ |cj| |
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var v1 = Vec(ci%10, ci//10) |
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var v2 = Vec(cj%10, cj//10) |
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v1.dist(v2) |
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}.map(1..100) |
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}.map(1..100) |
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TravelingSalesman::findpath(citydist, 1e6, 1)</lang> |
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{{out}} |
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<pre> |
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# Entropy(s₀) = 520.29 |
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# Random path: |
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1, 10, 79, 52, 24, 9, 58, 11, 42, 4 |
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15, 87, 62, 88, 21, 91, 99, 84, 61, 14 |
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5, 17, 33, 95, 74, 31, 40, 13, 37, 69 |
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6, 22, 97, 45, 56, 63, 75, 83, 53, 41 |
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3, 47, 89, 80, 78, 98, 46, 18, 25, 51 |
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93, 16, 50, 30, 48, 8, 66, 68, 59, 73 |
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49, 96, 36, 32, 100, 27, 76, 44, 64, 39 |
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90, 82, 20, 12, 54, 86, 29, 81, 26, 72 |
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60, 94, 35, 92, 43, 7, 85, 55, 28, 57 |
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23, 34, 65, 71, 38, 2, 77, 70, 19, 67 |
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1 |
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k: 100000 | T: 0.9000 | Eₛ: 185.1809 |
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k: 200000 | T: 0.8000 | Eₛ: 168.6262 |
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k: 300000 | T: 0.7000 | Eₛ: 146.5948 |
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k: 400000 | T: 0.6000 | Eₛ: 140.1441 |
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k: 500000 | T: 0.5000 | Eₛ: 129.5132 |
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k: 600000 | T: 0.4000 | Eₛ: 132.8942 |
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k: 700000 | T: 0.3000 | Eₛ: 124.2865 |
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k: 800000 | T: 0.2000 | Eₛ: 120.0859 |
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k: 900000 | T: 0.1000 | Eₛ: 115.0771 |
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k: 1000000 | T: 0.0000 | Eₛ: 114.9728 |
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k: 1000000 | T: 0.0000 | Eₛ: 114.9728 |
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# Found path: |
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1, 2, 13, 3, 4, 5, 6, 7, 8, 9 |
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19, 29, 18, 28, 27, 17, 16, 26, 25, 15 |
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14, 24, 23, 12, 11, 10, 20, 21, 30, 40 |
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41, 31, 32, 44, 45, 46, 47, 48, 49, 39 |
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38, 37, 36, 35, 34, 42, 51, 50, 60, 61 |
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52, 53, 54, 55, 56, 57, 58, 59, 69, 68 |
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77, 67, 66, 65, 64, 62, 72, 71, 70, 80 |
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81, 82, 74, 75, 76, 87, 88, 78, 79, 89 |
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99, 98, 97, 96, 86, 85, 83, 91, 90, 100 |
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92, 93, 94, 95, 84, 73, 63, 43, 33, 22 |
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1 |
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</pre> |
</pre> |
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Revision as of 16:03, 27 May 2019
Quoted from the Wikipedia page : Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Simulated annealing interprets slow cooling as a slow decrease in the probability of temporarily accepting worse solutions as it explores the solution space.
Pseudo code from Wikipedia
Notations : T : temperature. Decreases to 0. s : a system state E(s) : Energy at s. The function we want to minimize ∆E : variation of E, from state s to state s_next P(∆E , T) : Probability to move from s to s_next. if ( ∆E < 0 ) P = 1 else P = exp ( - ∆E / T) . Decreases as T → 0 Pseudo-code: Let s = s0 -- initial state For k = 0 through kmax (exclusive): T ← temperature(k , kmax) Pick a random neighbour state , s_next ← neighbour(s) ∆E ← E(s) - E(s_next) If P(∆E , T) ≥ random(0, 1), move to the new state: s ← s_next Output: the final state s
Problem statement
We want to apply SA to the travelling salesman problem. There are 100 cities, numbered 0 to 99, located on a plane, at integer coordinates i,j : 0 <= i,j < 10 . The city at (i,j) has number 10*i + j. The cities are all connected : the graph is complete : you can go from one city to any other city in one step.
The salesman wants to start from city 0, visit all cities, each one time, and go back to city 0. The travel cost between two cities is the euclidian distance between there cities. The total travel cost is the total path length.
A path s is a sequence (0 a b ...z 0) where (a b ..z) is a permutation of the numbers (1 2 .. 99). The path length = E(s) is the sum d(0,a) + d(a,b) + ... + d(z,0) , where d(u,v) is the distance between two cities. Naturally, we want to minimize E(s).
Definition : The neighbours of a city are the closest cities at distance 1 horizontally/vertically, or √2 diagonally. A corner city (0,9,90,99) has 3 neighbours. A center city has 8 neighbours.
Distances between cities d ( 0, 7) → 7 d ( 0, 99) → 12.7279 d ( 23, 78) → 7.0711 d ( 33, 44) → 1.4142 // sqrt(2)
Task
Apply SA to the travelling salesman problem, using the following set of parameters/functions :
- kT = 1
- temperature (k, kmax) = kT * (1 - k/kmax)
- neighbour (s) : Pick a random city u > 0 . Pick a random neighbour city v > 0 of u , among u's 8 (max) neighbours on the grid. Swap u and v in s . This gives the new state s_next.
- kmax = 1000_000
- s0 = a random permutation
For k = 0 to kmax by step kmax/10 , display k, T, E(s). Display the final state s_final, and E(s_final).
You will see that the Energy may grow to a local optimum, before decreasing to a global optimum.
Illustrated example Temperature charts
Numerical example
kT = 1 E(s0) = 529.9158 k: 0 T: 1 Es: 529.9158 k: 100000 T: 0.9 Es: 201.1726 k: 200000 T: 0.8 Es: 178.1723 k: 300000 T: 0.7 Es: 154.7069 k: 400000 T: 0.6 Es: 158.1412 <== local optimum k: 500000 T: 0.5 Es: 133.856 k: 600000 T: 0.4 Es: 129.5684 k: 700000 T: 0.3 Es: 112.6919 k: 800000 T: 0.2 Es: 105.799 k: 900000 T: 0.1 Es: 102.8284 k: 1000000 T: 0 Es: 102.2426 E(s_final) = 102.2426 Path s_final = ( 0 10 11 21 31 20 30 40 50 60 70 80 90 91 81 71 73 83 84 74 64 54 55 65 75 76 66 67 77 78 68 58 48 47 57 56 46 36 37 27 26 16 15 5 6 7 17 18 8 9 19 29 28 38 39 49 59 69 79 89 99 98 88 87 97 96 86 85 95 94 93 92 82 72 62 61 51 41 42 52 63 53 43 32 22 12 13 23 33 34 44 45 35 25 24 14 4 3 2 1 0)
Extra credit
Tune the parameters kT, kmax, or use different temperature() and/or neighbour() functions to demonstrate a quicker convergence, or a better optimum.
EchoLisp
<lang scheme> (lib 'math)
- distances
(define (d ci cj) (distance (% ci 10) (quotient ci 10) (% cj 10) (quotient cj 10))) (define _dists (build-vector 10000 (lambda (ij) (d (quotient ij 100) (% ij 100))))) (define-syntax-rule (dist ci cj) [_dists (+ ci (* 100 cj))])
- E(s) = length(path)
(define (Es path) (define lpath (vector->list path)) (for/sum ((ci lpath) (cj (rest lpath))) (dist ci cj)))
- temperature() function
(define (T k kmax kT) (* kT (- 1 (// k kmax))))
- |
- alternative temperature()
- must be decreasing with k increasing and → 0
(define (T k kmax kT) (* kT (- 1 (sin (* PI/2 (// k kmax)))))) |#
- ∆E = Es_new - Es_old > 0
- probability to move if ∆E > 0, → 0 when T → 0 (frozen state)
(define (P ∆E k kmax kT) (exp (// (- ∆E ) (T k kmax kT))))
- ∆E from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..)
- ∆E before swapping (u,v)
- Quicker than Es(s_next) - Es(s)
(define (dE s u v)
- old
(define a (dist [s (1- u)] [s u])) (define b (dist [s (1+ u)] [s u])) (define c (dist [s (1- v)] [s v])) (define d (dist [s (1+ v)] [s v]))
- new
(define na (dist [s (1- u)] [s v])) (define nb (dist [s (1+ u)] [s v])) (define nc (dist [s (1- v)] [s u])) (define nd (dist [s (1+ v)] [s u]))
(cond ((= v (1+ u)) (- (+ na nd) (+ a d))) ((= u (1+ v)) (- (+ nc nb) (+ c b))) (else (- (+ na nb nc nd) (+ a b c d)))))
- all 8 neighbours
(define dirs #(1 -1 10 -10 9 11 -11 -9))
(define (sa kmax (kT 10)) (define s (list->vector (cons 0 (append (shuffle (range 1 100)) 0)))) (printf "E(s0) %d" (Es s)) ;; random starter (define Emin (Es s)) ;; E0
(for ((k kmax)) (when (zero? (% k (/ kmax 10))) (printf "k: %10d T: %8.4d Es: %8.4d" k (T k kmax kT) (Es s)) )
(define u (1+ (random 99))) ;; city index 1 99 (define cv (+ [s u] [dirs (random 8)])) ;; city number #:continue (or (> cv 99) (<= cv 0)) #:continue (> (dist [s u] cv) 5) ;; check true neighbour (eg 0 9) (define v (vector-index cv s 1)) ;; city index
(define ∆e (dE s u v)) (when (or (< ∆e 0) ;; always move if negative (>= (P ∆e k kmax kT) (random))) (vector-swap! s u v) (+= Emin ∆e))
;; (assert (= (round Emin) (round (Es s)))) ) ;; for
(printf "k: %10d T: %8.4d Es: %8.4d" kmax (T (1- kmax) kmax kT) (Es s)) (s-plot s 0) (printf "E(s_final) %d" Emin) (writeln 'Path s)) </lang>
- Output:
(sa 1000000 1) E(s0) 501.0909 k: 0 T: 1 Es: 501.0909 k: 100000 T: 0.9 Es: 167.3632 k: 200000 T: 0.8 Es: 160.7791 k: 300000 T: 0.7 Es: 166.8746 k: 400000 T: 0.6 Es: 142.579 k: 500000 T: 0.5 Es: 131.0657 k: 600000 T: 0.4 Es: 116.9214 k: 700000 T: 0.3 Es: 110.8569 k: 800000 T: 0.2 Es: 103.3137 k: 900000 T: 0.1 Es: 102.4853 k: 1000000 T: 0 Es: 102.4853 E(s_final) 102.4853 Path #( 0 10 20 30 40 50 60 70 71 61 62 53 63 64 54 44 45 55 65 74 84 83 73 72 82 81 80 90 91 92 93 94 95 85 75 76 86 96 97 98 99 88 89 79 69 59 49 48 47 57 58 68 78 87 77 67 66 56 46 36 35 25 24 34 33 32 43 42 52 51 41 31 21 11 12 22 23 13 14 15 16 17 26 27 37 38 39 29 28 18 19 9 8 7 6 5 4 3 2 1 0)
Go
<lang go>package main
import (
"fmt" "math" "math/rand" "time"
)
var (
dists = calcDists() dirs = [8]int{1, -1, 10, -10, 9, 11, -11, -9} // all 8 neighbors
)
// distances func calcDists() []float64 {
dists := make([]float64, 10000) for i := 0; i < 10000; i++ { ab, cd := math.Floor(float64(i)/100), float64(i%100) a, b := math.Floor(ab/10), float64(int(ab)%10) c, d := math.Floor(cd/10), float64(int(cd)%10) dists[i] = math.Hypot(a-c, b-d) } return dists
}
// index into lookup table of float64s func dist(ci, cj int) float64 {
return dists[cj*100+ci]
}
// energy at s, to be minimized func Es(path []int) float64 {
d := 0.0 for i := 0; i < len(path)-1; i++ { d += dist(path[i], path[i+1]) } return d
}
// temperature function, decreases to 0 func T(k, kmax, kT int) float64 {
return (1 - float64(k)/float64(kmax)) * float64(kT)
}
// variation of E, from state s to state s_next func dE(s []int, u, v int) float64 {
su, sv := s[u], s[v] // old a, b, c, d := dist(s[u-1], su), dist(s[u+1], su), dist(s[v-1], sv), dist(s[v+1], sv) // new na, nb, nc, nd := dist(s[u-1], sv), dist(s[u+1], sv), dist(s[v-1], su), dist(s[v+1], su) if v == u+1 { return (na + nd) - (a + d) } else if u == v+1 { return (nc + nb) - (c + b) } else { return (na + nb + nc + nd) - (a + b + c + d) }
}
// probability to move from s to s_next func P(deltaE float64, k, kmax, kT int) float64 {
return math.Exp(-deltaE / T(k, kmax, kT))
}
func sa(kmax, kT int) {
rand.Seed(time.Now().UnixNano()) temp := make([]int, 99) for i := 0; i < 99; i++ { temp[i] = i + 1 } rand.Shuffle(len(temp), func(i, j int) { temp[i], temp[j] = temp[j], temp[i] }) s := make([]int, 101) // all 0 by default copy(s[1:], temp) // random path from 0 to 0 fmt.Println("kT =", kT) fmt.Printf("E(s0) %f\n\n", Es(s)) // random starter Emin := Es(s) // E0 for k := 0; k <= kmax; k++ { if k%(kmax/10) == 0 { fmt.Printf("k:%10d T: %8.4f Es: %8.4f\n", k, T(k, kmax, kT), Es(s)) } u := 1 + rand.Intn(99) // city index 1 to 99 cv := s[u] + dirs[rand.Intn(8)] // city number if cv <= 0 || cv >= 100 { // bogus city continue } if dist(s[u], cv) > 5 { // check true neighbor (eg 0 9) continue } v := s[cv] // city index deltae := dE(s, u, v) if deltae < 0 || // always move if negative P(deltae, k, kmax, kT) >= rand.Float64() { s[u], s[v] = s[v], s[u] Emin += deltae } } fmt.Printf("\nE(s_final) %f\n", Emin) fmt.Println("Path:") // output final state for i := 0; i < len(s); i++ { if i > 0 && i%10 == 0 { fmt.Println() } fmt.Printf("%4d", s[i]) } fmt.Println()
}
func main() {
sa(1e6, 1)
}</lang>
- Output:
Sample run:
kT = 1 E(s0) 520.932463 k: 0 T: 1.0000 Es: 520.9325 k: 100000 T: 0.9000 Es: 185.1279 k: 200000 T: 0.8000 Es: 167.7657 k: 300000 T: 0.7000 Es: 158.6923 k: 400000 T: 0.6000 Es: 151.6564 k: 500000 T: 0.5000 Es: 139.9185 k: 600000 T: 0.4000 Es: 132.9964 k: 700000 T: 0.3000 Es: 121.8962 k: 800000 T: 0.2000 Es: 120.0445 k: 900000 T: 0.1000 Es: 116.8476 k: 1000000 T: 0.0000 Es: 116.5565 E(s_final) 116.556509 Path: 0 11 21 31 41 51 52 61 62 72 82 73 74 64 44 45 55 54 63 53 42 32 43 33 35 34 24 23 22 13 12 2 3 4 14 25 26 7 6 16 15 5 17 27 36 46 56 66 65 75 77 78 68 69 59 49 39 38 37 28 29 19 9 8 18 47 48 58 57 67 76 86 85 95 96 97 87 88 79 89 99 98 84 94 83 93 92 91 90 80 81 71 70 60 50 40 30 20 10 1 0
J
Implementation:
<lang J>dist=: +/&.:*:@:-"1/~10 10#:i.100
satsp=:4 :0
kT=. 1 pathcost=. [: +/ 2 {&y@<\ 0 , ] , 0: neighbors=. 0 (0}"1) y e. 1 2{/:~~.,y s=. (?~#y)-.0 d=. pathcost s step=. x%10 for_k. i.x+1 do. T=. kT*1-k%x u=. ({~ ?@#)s v=. ({~ ?@#)I.u{neighbors sk=. (?0 do. s=.sk d=.dk end. if. 0=step|k do. echo k,T,d end. end. 0,s,0
)</lang>
Notes:
E(s_final) gets displayed on the kmax progress line.
We do not do anything special for negative deltaE because the exponential will be greater than 1 for that case and that will always be greater than our random number from the range 0..1.
Also, while we leave connection distances (and, thus, number of cities) as a parameter, some other aspects of this problem made more sense when included in the implementation:
We leave city 0 out of our data structure, since it can't appear in the middle of our path. But we bring it back in when computing path distance.
Neighbors are any city which have one of the two closest non-zero distances from the current city (and specifically excluding city 0, since that is anchored as our start and end city).
Sample run:
<lang J> 1e6 satsp dist 0 1 538.409 100000 0.9 174.525 200000 0.8 165.541 300000 0.7 173.348 400000 0.6 168.188 500000 0.5 134.983 600000 0.4 121.585 700000 0.3 111.443 800000 0.2 101.657 900000 0.1 101.657 1e6 0 101.657 0 1 2 3 4 13 23 24 34 44 43 33 32 31 41 42 52 51 61 62 53 54 64 65 55 45 35 25 15 14 5 6 7 17 16 26 27 37 36 46 47 48 38 28 18 8 9 19 29 39 49 59 69 79 78 68 58 57 56 66 67 77 76 75 85 86 87 88 89 99 98 97 96 95 94 84 74 73 63 72 82 83 93 92 91 90 80 81 71 70 60 50 40 30 20 21 22 12 11 10 0</lang>
Julia
Module: <lang julia>module TravelingSalesman
using Random, Printf
- Eₛ: length(path)
Eₛ(distances, path) = sum(distances[ci, cj] for (ci, cj) in zip(path, Iterators.drop(path, 1)))
- T: temperature
T(k, kmax, kT) = kT * (1 - k / kmax)
- Alternative temperature:
- T(k, kmax, kT) = kT * (1 - sin(π / 2 * k / kmax))
- ΔE = Eₛ_new - Eₛ_old > 0
- Prob. to move if ΔE > 0, → 0 when T → 0 (fronzen state)
P(ΔE, k, kmax, kT) = exp(-ΔE / T(k, kmax, kT))
- ∆E from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..)
- ∆E before swapping (u,v)
- Quicker than Eₛ(s_next) - Eₛ(path)
function dE(distances, path, u, v)
a = distances[path[u - 1], path[u]] b = distances[path[u + 1], path[u]] c = distances[path[v - 1], path[v]] d = distances[path[v + 1], path[v]]
na = distances[path[u - 1], path[v]] nb = distances[path[u + 1], path[v]] nc = distances[path[v - 1], path[u]] nd = distances[path[v + 1], path[u]]
if v == u + 1 return (na + nd) - (a + d) elseif u == v + 1 return (nc + nb) - (c + b) else return (na + nb + nc + nd) - (a + b + c + d) end
end
const dirs = [1, -1, 10, -10, 9, 11, -11, -9]
function _prettypath(path)
r = IOBuffer() for g in Iterators.partition(path, 10) println(r, join(lpad.(g, 3), ", ")) end return String(take!(r))
end
function findpath(distances, kmax, kT)
n = size(distances, 1) path = vcat(1, shuffle(2:n), 1) Emin = Eₛ(distances, path) @printf("\n# Entropy(s₀) = %10.2f\n", Emin) println("# Random path: \n", _prettypath(path))
for k in Base.OneTo(kmax) if iszero(k % (kmax ÷ 10)) @printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", k, T(k, kmax, kT), Eₛ(distances, path)) end u = rand(2:n) v = path[u] + rand(dirs) v ∈ 2:n || continue
δE = dE(distances, path, u, v) if δE < 0 || P(δE, k, kmax, kT) ≥ rand() path[u], path[v] = path[v], path[u] Emin += δE end end
@printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", kmax, T(kmax, kmax, kT), Eₛ(distances, path)) println("\n# Found path:\n", _prettypath(path)) return path
end
end # module TravelingSalesman</lang>
Main: <lang julia>distance(a, b) = sqrt(sum((a .- b) .^ 2)) const _citydist = collect(distance((ci % 10, ci ÷ 10), (cj % 10, cj ÷ 10)) for ci in 1:100, cj in 1:100)
TravelingSalesman.findpath(_citydist, 1_000_000, 1)</lang>
- Output:
# Entropy(s₀) = 521.86 # Random path: 1, 2, 11, 80, 78, 73, 68, 19, 43, 69 86, 79, 66, 67, 77, 96, 26, 62, 60, 98 71, 3, 59, 37, 18, 40, 34, 92, 97, 6 84, 94, 29, 63, 36, 50, 87, 45, 83, 90 76, 28, 15, 38, 91, 58, 47, 44, 85, 17 25, 33, 31, 99, 27, 74, 53, 95, 16, 13 42, 88, 8, 4, 7, 64, 54, 9, 14, 41 5, 81, 65, 23, 75, 100, 89, 51, 20, 48 82, 12, 21, 55, 24, 70, 49, 10, 35, 72 52, 22, 61, 32, 46, 57, 30, 93, 39, 56 1 k: 100000 | T: 0.9000 | Eₛ: 184.4448 k: 200000 | T: 0.8000 | Eₛ: 175.3662 k: 300000 | T: 0.7000 | Eₛ: 169.0505 k: 400000 | T: 0.6000 | Eₛ: 160.8328 k: 500000 | T: 0.5000 | Eₛ: 147.1973 k: 600000 | T: 0.4000 | Eₛ: 132.9186 k: 700000 | T: 0.3000 | Eₛ: 126.9931 k: 800000 | T: 0.2000 | Eₛ: 122.0656 k: 900000 | T: 0.1000 | Eₛ: 119.7924 k: 1000000 | T: 0.0000 | Eₛ: 119.7924 k: 1000000 | T: 0.0000 | Eₛ: 119.7924 # Found path: 1, 2, 12, 13, 3, 4, 6, 7, 8, 9 19, 18, 17, 5, 14, 15, 16, 27, 28, 29 39, 38, 26, 25, 24, 23, 22, 10, 21, 20 30, 31, 32, 33, 34, 35, 36, 37, 49, 48 47, 46, 45, 44, 43, 42, 41, 40, 50, 51 52, 53, 54, 55, 56, 57, 58, 59, 69, 68 67, 65, 64, 63, 62, 61, 71, 60, 70, 80 81, 82, 72, 73, 74, 66, 78, 79, 89, 99 98, 97, 96, 95, 94, 85, 86, 87, 88, 77 76, 75, 84, 83, 93, 92, 91, 100, 90, 11 1
Nim
<lang Nim>import math, random, sugar, strformat from times import cpuTime
const
kT = 1 kMax = 1_000_000
proc randomNeighbor(x: int): int =
case x of 0: rand([1, 10, 11]) of 9: rand([8, 18, 19]) of 90: rand([80, 81, 91]) of 99: rand([88, 89, 98]) elif x > 0 and x < 9: # top ceiling rand [x-1, x+1, x+9, x+10, x+11] elif x > 90 and x < 99: # bottom floor rand [x-11, x-10, x-9, x-1, x+1] elif x mod 10 == 0: # left wall rand([x-10, x-9, x+1, x+10, x+11]) elif (x+1) mod 10 == 0: # right wall rand([x-11, x-10, x-1, x+9, x+10]) else: # center rand([x-11, x-10, x-9, x-1, x+1, x+9, x+10, x+11])
proc neighbor(s: seq[int]): seq[int] =
result = s var city = rand s var cityNeighbor = city.randomNeighbor while cityNeighbor == 0 or city == 0: city = rand s cityNeighbor = city.randomNeighbor result[s.find city].swap result[s.find cityNeighbor]
func distNeighbor(a, b: int): float =
template divmod(a: int): (int, int) = (a div 10, a mod 10) let (diva, moda) = a.divmod (divb, modb) = b.divmod hypot((diva-divb).float, (moda-modb).float)
func temperature(k, kmax: float): float =
kT * (1 - (k / kmax))
func pdelta(eDelta, temp: float): float =
if eDelta < 0: 1.0 else: exp(-eDelta / temp)
func energy(path: seq[int]): float =
var sum = 0.distNeighbor path[0] for i in 1 ..< path.len: sum += path[i-1].distNeighbor(path[i]) sum + path[^1].distNeighbor 0
proc main =
randomize() var s = block: var x = lc[x | (x <- 0 .. 99), int] template shuffler: int = rand(1 .. x.len-1) for i in 1 .. x.len-1: x[i].swap x[shuffler()] x let startTime = cpuTime() echo fmt"E(s0): {energy s:6.4f}" for k in 0 .. kMax: var temp = temperature(float k, float kMax) lastenergy = energy s newneighbor = s.neighbor newenergy = newneighbor.energy if k mod (kMax div 10) == 0: echo fmt"k: {k:7} T: {temp:6.2f} Es: {lastenergy:6.4f}" var deltaEnergy = newenergy - lastenergy if pDelta(deltaEnergy, temp) >= rand(1.0): s = newneighbor
s.add 0 echo fmt"E(sFinal): {energy s:6.4f}" echo fmt"path: {s}" #echo fmt"ended after: {cpuTime() - startTime}"
main()</lang>
Compile and run:
nim c -r -d:release --opt:speed travel_sa.nim
- Output:
Sample run:
E(s0): 505.1591 k: 0 T: 1.00 Es: 505.1591 k: 100000 T: 0.90 Es: 196.5216 k: 200000 T: 0.80 Es: 165.6735 k: 300000 T: 0.70 Es: 159.3411 k: 400000 T: 0.60 Es: 144.8330 k: 500000 T: 0.50 Es: 131.7888 k: 600000 T: 0.40 Es: 127.6914 k: 700000 T: 0.30 Es: 113.9280 k: 800000 T: 0.20 Es: 104.7279 k: 900000 T: 0.10 Es: 103.3137 k: 1000000 T: 0.00 Es: 103.3137 E(sFinal): 103.3137 path: @[0, 10, 11, 22, 21, 20, 30, 31, 41, 40, 50, 51, 61, 60, 70, 71, 81, 80, 90, 91, 92, 93, 82, 83, 73, 72, 62, 63, 53, 52, 42, 32, 33, 23, 13, 14, 24, 34, 35, 25, 15, 16, 26, 36, 47, 48, 38, 39, 49, 59, 58, 57, 68, 69, 79, 89, 99, 98, 97, 96, 95, 94, 84, 74, 75, 85, 86, 87, 88, 78, 77, 67, 76, 66, 65, 64, 54, 43, 44, 45, 55, 56, 46, 37, 27, 28, 29, 19, 9, 8, 18, 17, 7, 6, 5, 4, 3, 2, 12, 1, 0]
Phix
Note that the standard builtin exp() suffered occasional overflows, so this uses b_a_exp() from bigatom.e, but it does make it much slower. <lang Phix>function hypot(atom a,b) return sqrt(a*a+b*b) end function
function calc_dists()
sequence dists = repeat(0,10000) for abcd=1 to 10000 do integer {ab,cd} = {floor(abcd/100),mod(abcd,100)}, {a,b,c,d} = {floor(ab/10),mod(ab,10), floor(cd/10),mod(cd,10)} dists[abcd] = hypot(a-c,b-d) end for return dists
end function constant dists = calc_dists()
function dist(integer ci,cj) return dists[cj*100+ci] end function
function Es(sequence path)
atom d = 0 for i=1 to length(path)-1 do d += dist(path[i],path[i+1]) end for return d
end function
-- temperature() function function T(integer k, kmax, kT) return (1-k/kmax)*kT end function
include bigatom.e -- (just for b_a_exp())
-- deltaE = Es_new - Es_old > 0 -- probability to move if deltaE > 0, -->0 when T --> 0 (frozen state) function P(atom deltaE, integer k, kmax, kT) return b_a_exp(-deltaE/T(k,kmax,kT)) end function
-- deltaE from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..) function dE(sequence s, integer u,v) -- (note that u,v are 0-based, but 1..99 here) -- integer sum1 = s[u-1], su = s[u], sup1 = s[u+1], -- svm1 = s[v-1], sv = s[v], svp1 = s[v+1]
integer sum1 = s[u], su = s[u+1], sup1 = s[u+2], svm1 = s[v], sv = s[v+1], svp1 = s[v+2] -- old atom {a,b,c,d}:={dist(sum1,su), dist(su,sup1), dist(svm1,sv), dist(sv,svp1)}, -- new {na,nb,nc,nd}:={dist(sum1,sv), dist(sv,sup1), dist(svm1,su), dist(su,svp1)} return iff(v==u+1?(na+nd)-(a+d): iff(u==v+1?(nc+nb)-(c+b): (na+nb+nc+nd)-(a+b+c+d)))
end function
-- all 8 neighbours constant dirs = {1, -1, 10, -10, 9, 11, -11, -9}
procedure sa(integer kmax, kT=10)
sequence s = 0&shuffle(tagset(99))&0 atom Emin:=Es(s) -- E0 printf(1,"E(s0) %f\n",Emin) -- random starter for k=0 to kmax do if mod(k,kmax/10)=0 then printf(1,"k:%,10d T: %8.4f Es: %8.4f\n",{k,T(k,kmax,kT),Es(s)}) end if integer u = rand(99), -- city index 1 99 cv = s[u+1]+dirs[rand(8)] -- city number if cv>0 and cv<100 -- not bogus city and dist(s[u+1],cv)<5 then -- and true neighbour integer v = s[cv+1] -- city index atom deltae := dE(s,u,v); if deltae<0 -- always move if negative or P(deltae,k,kmax,kT)>=rnd() then {s[u+1],s[v+1]} = {s[v+1],s[u+1]} Emin += deltae end if end if end for printf(1,"E(s_final) %f\n",Emin) printf(1,"Path:\n") pp(s,{pp_IntFmt,"%2d",pp_StrFmt,-2})
end procedure sa(1_000_000,1)</lang>
- Output:
E(s0) 515.164811 k: 0 T: 1.0000 Es: 515.1648 k: 100,000 T: 0.9000 Es: 189.3123 k: 200,000 T: 0.8000 Es: 198.7498 k: 300,000 T: 0.7000 Es: 158.2189 k: 400,000 T: 0.6000 Es: 165.4813 k: 500,000 T: 0.5000 Es: 156.3467 k: 600,000 T: 0.4000 Es: 142.7928 k: 700,000 T: 0.3000 Es: 128.0352 k: 800,000 T: 0.2000 Es: 121.7794 k: 900,000 T: 0.1000 Es: 121.2328 k: 1,000,000 T: 0.0000 Es: 121.1291 E(s_final) 121.129115 Path: { 0,10,62,63,64,65,76,75,84,85,95,86,96,97,87,77,67,66,56,46,47,48,49,59,69, 79,89,99,98,88,78,68,58,57,37,38,27,26,36,35,45,55,54,53,52,43,33,23,22,32, 42,41,51,61,60,50,40,30,31,21,20,11,12, 2, 3, 4, 5, 6,17,18,28,39,29,19, 9, 8, 7,16,15,24,44,74,83,93,94,92,91,71,70,90,80,81,82,72,73,34,25,14,13, 1, 0}
Sidef
<lang ruby>module TravelingSalesman {
# Eₛ: length(path) func Eₛ(distances, path) { var total = 0 [path, path.slice(1)].zip {|ci,cj| total += distances[ci-1][cj-1] } total }
# T: temperature func T(k, kmax, kT) { kT * (1 - k/kmax) }
# ΔE = Eₛ_new - Eₛ_old > 0 # Prob. to move if ΔE > 0, → 0 when T → 0 (fronzen state) func P(ΔE, k, kmax, kT) { exp(-ΔE / T(k, kmax, kT)) }
# ∆E from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..) # ∆E before swapping (u,v) # Quicker than Eₛ(s_next) - Eₛ(path) func dE(distances, path, u, v) {
var a = distances[path[u-1]-1][path[u]-1] var b = distances[path[u+1]-1][path[u]-1] var c = distances[path[v-1]-1][path[v]-1] var d = distances[path[v+1]-1][path[v]-1]
var na = distances[path[u-1]-1][path[v]-1] var nb = distances[path[u+1]-1][path[v]-1] var nc = distances[path[v-1]-1][path[u]-1] var nd = distances[path[v+1]-1][path[u]-1]
if (v == u+1) { return ((na+nd) - (a+d)) }
if (u == v+1) { return ((nc+nb) - (c+b)) }
return ((na+nb+nc+nd) - (a+b+c+d)) }
const dirs = [1, -1, 10, -10, 9, 11, -11, -9]
func _prettypath(path) { path.slices(10).map { .map{ "%3s" % _ }.join(', ') }.join("\n") }
func findpath(distances, kmax, kT) {
const n = distances.len const R = 2..n
var path = [1, R.shuffle..., 1] var Emin = Eₛ(distances, path)
printf("# Entropy(s₀) = s%10.2f\n", Emin) printf("# Random path:\n%s\n\n", _prettypath(path))
for k in (1 .. kmax) {
if (k % (kmax//10) == 0) { printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", k, T(k, kmax, kT), Eₛ(distances, path)) }
var u = R.rand var v = (path[u-1] + dirs.rand) v ~~ R || next
var δE = dE(distances, path, u-1, v-1) if ((δE < 0) || (P(δE, k, kmax, kT) >= 1.rand)) { path.swap(u-1, v-1) Emin += δE } }
printf("k: %10d | T: %8.4f | Eₛ: %8.4f\n", kmax, T(kmax, kmax, kT), Eₛ(distances, path)) say ("\n# Found path:\n", _prettypath(path)) return path }
}
var citydist = {|ci|
{ |cj| var v1 = Vec(ci%10, ci//10) var v2 = Vec(cj%10, cj//10) v1.dist(v2) }.map(1..100)
}.map(1..100)
TravelingSalesman::findpath(citydist, 1e6, 1)</lang>
- Output:
# Entropy(s₀) = 520.29 # Random path: 1, 10, 79, 52, 24, 9, 58, 11, 42, 4 15, 87, 62, 88, 21, 91, 99, 84, 61, 14 5, 17, 33, 95, 74, 31, 40, 13, 37, 69 6, 22, 97, 45, 56, 63, 75, 83, 53, 41 3, 47, 89, 80, 78, 98, 46, 18, 25, 51 93, 16, 50, 30, 48, 8, 66, 68, 59, 73 49, 96, 36, 32, 100, 27, 76, 44, 64, 39 90, 82, 20, 12, 54, 86, 29, 81, 26, 72 60, 94, 35, 92, 43, 7, 85, 55, 28, 57 23, 34, 65, 71, 38, 2, 77, 70, 19, 67 1 k: 100000 | T: 0.9000 | Eₛ: 185.1809 k: 200000 | T: 0.8000 | Eₛ: 168.6262 k: 300000 | T: 0.7000 | Eₛ: 146.5948 k: 400000 | T: 0.6000 | Eₛ: 140.1441 k: 500000 | T: 0.5000 | Eₛ: 129.5132 k: 600000 | T: 0.4000 | Eₛ: 132.8942 k: 700000 | T: 0.3000 | Eₛ: 124.2865 k: 800000 | T: 0.2000 | Eₛ: 120.0859 k: 900000 | T: 0.1000 | Eₛ: 115.0771 k: 1000000 | T: 0.0000 | Eₛ: 114.9728 k: 1000000 | T: 0.0000 | Eₛ: 114.9728 # Found path: 1, 2, 13, 3, 4, 5, 6, 7, 8, 9 19, 29, 18, 28, 27, 17, 16, 26, 25, 15 14, 24, 23, 12, 11, 10, 20, 21, 30, 40 41, 31, 32, 44, 45, 46, 47, 48, 49, 39 38, 37, 36, 35, 34, 42, 51, 50, 60, 61 52, 53, 54, 55, 56, 57, 58, 59, 69, 68 77, 67, 66, 65, 64, 62, 72, 71, 70, 80 81, 82, 74, 75, 76, 87, 88, 78, 79, 89 99, 98, 97, 96, 86, 85, 83, 91, 90, 100 92, 93, 94, 95, 84, 73, 63, 43, 33, 22 1
zkl
<lang zkl>var [const] _dists=(0d10_000).pump(List,fcn(abcd){ // two points (a,b) & (c,d), calc distance
ab,cd,a,b,c,d:=abcd/100, abcd%100, ab/10,ab%10, cd/10,cd%10; (a-c).toFloat().hypot(b-d)
}); fcn dist(ci,cj){ _dists[cj*100 + ci] } // index into lookup table of floats
fcn Es(path) // E(s) = length(path): E(a,b,c)--> dist(a,b) + dist(b,c)
{ d:=Ref(0.0); path.reduce('wrap(a,b){ d.apply('+,dist(a,b)); b }); d.value }
// temperature() function fcn T(k,kmax,kT){ (1.0 - k.toFloat()/kmax)*kT }
// deltaE = Es_new - Es_old > 0 // probability to move if deltaE > 0, -->0 when T --> 0 (frozen state) fcn P(deltaE,k,kmax,kT){ (-deltaE/T(k,kmax,kT)).exp() } //-->Float
// deltaE from path ( .. a u b .. c v d ..) to (.. a v b ... c u d ..) // deltaE before swapping (u,v) fcn dE(s,u,v){ su,sv:=s[u],s[v]; //-->Float
// old a,b,c,d:=dist(s[u-1],su), dist(s[u+1],su), dist(s[v-1],sv), dist(s[v+1],sv); // new na,nb,nc,nd:=dist(s[u-1],sv), dist(s[u+1],sv), dist(s[v-1],su), dist(s[v+1],su);
if (v==u+1) (na+nd) - (a+d); else if(u==v+1) (nc+nb) - (c+b); else (na+nb+nc+nd) - (a+b+c+d);
}
// all 8 neighbours var [const] dirs=ROList(1, -1, 10, -10, 9, 11, -11, -9),
fmt="k:%10,d T: %8.4f Es: %8.4f".fmt; // since we use it twice
fcn sa(kmax,kT=10){
s:=List(0, [1..99].walk().shuffle().xplode(), 0); // random path from 0 to 0 println("E(s0) %f".fmt(Es(s))); // random starter Emin:=Es(s); // E0 foreach k in (kmax){ if(0==k%(kmax/10)) println(fmt(k,T(k,kmax,kT),Es(s))); u:=(1).random(100); // city index 1 99 cv:=s[u] + dirs[(0).random(8)]; // city number if(not (0<cv<100)) continue; // bogus city if(dist(s[u],cv)>5) continue; // check true neighbour (eg 0 9) v:=s.index(cv,1); // city index deltae:=dE(s,u,v); if(deltae<0 or // always move if negative
P(deltae,k,kmax,kT)>=(0.0).random(1)){ s.swap(u,v); Emin+=deltae;
} // (assert (= (round Emin) (round (Es s)))) }//foreach println(fmt(kmax,T(kmax-1,kmax,kT),Es(s))); println("E(s_final) %f".fmt(Emin)); println("Path: ",s.toString(*));
}</lang> <lang zkl>sa(0d1_000_000,1);</lang>
- Output:
E(s0) 540.897080 k: 0 T: 1.0000 Es: 540.8971 k: 100,000 T: 0.9000 Es: 181.5102 k: 200,000 T: 0.8000 Es: 167.1944 k: 300,000 T: 0.7000 Es: 159.0975 k: 400,000 T: 0.6000 Es: 170.2344 k: 500,000 T: 0.5000 Es: 130.9919 k: 600,000 T: 0.4000 Es: 115.3422 k: 700,000 T: 0.3000 Es: 113.9280 k: 800,000 T: 0.2000 Es: 106.7924 k: 900,000 T: 0.1000 Es: 103.7213 k: 1,000,000 T: 0.0000 Es: 103.7213 E(s_final) 103.721349 Path: L(0,10,11,21,20,30,40,50,60,70,80,81,71,72,73,63,52,62,61,51,41,31,32,22,12,13,14,15,25,16,17,18,28,27,26,36,35,45,34,24,23,33,42,43,44,54,53,64,74,84,83,82,90,91,92,93,94,95,85,86,96,97,87,88,98,99,89,79,69,68,78,77,67,66,76,75,65,55,56,46,37,38,48,47,57,58,59,49,39,29,19,9,8,7,6,5,4,3,2,1,0)