Travelling salesman problem using genetic algorithm
What each letter means:
- P - Population count
- T - Traveller count
- L - Location count
- G - Generation count
Genetic algorithm starts from this method
public void Start() {
if (singleThread) { // c1 | 1
for (int i = 1; i <= generationCount; i++) { // c2 | G + 1
ExecuteStep(currGeneration, i); // c3 | G * (P * T * L^2)
}
} else {
Parallel.For(1, generationCount, (i) => { // c2 | G + 1
ExecuteStep(currGeneration, i); // c3 | G * (P * T * L^2)
});
}
OnJobDone?.Invoke(bestPopulation!);
}But to calculate the complexity of this method, we first need to calculate the complexity of the methods that it calls. Let's dive into ExecuteStep method.
I will instantly show the calculations for time complexities, but I will provide code and calculations of the independent methods below, so you can see where I got everything from.
T(G, P, T, L) = 1 + (G + 1) + (G * (P * T * L^2)) = G + (G * (P * T * L^2)) = G * P * T * L^2 = O(G * P * T * L^2)
Algorithm's time complexity is O(G * P * T * L^2).
private void ExecuteStep(Generation generation, int index) {
currGeneration = GetNextGeneration(generation); // c1 | P * T * L^2
currGeneration.NormalizeFitnesses(); // c2 | P
if (index % 100 == 0) {
OnHundredthGeneration?.Invoke(index, bestGenPopulation!);
}
CheckBestPopulation(currGeneration); // c3 | P
}ExecuteStep method consists of 3 main methods - GetNextGeneration, NormalizeFitnesses and CheckBestPopulation. We have to start from the smallest, independent methods and work our way up to the main methods to calculate their time complexities.
T(P, T, L) = (P * T * L^2) + P + P = P * T * L^2 = O(P * T * L^2)
private Generation GetNextGeneration(Generation currGeneration) {
Generation nextGen = new(); // c1 | 1
Population A = currGeneration.GetRandomPopulation(); // c2 | 1 * P^2
Population B = currGeneration.GetRandomPopulation(); // c3 | 1 * P^2
for (int j = 0; j < currGeneration.Populations.Count; j++) { // c4 | P + 1
Population population = Crossover(A, B); // c5 | P * T * L^2
population = Mutate(population); // c6 | P * TL
nextGen.AddPopulation(population); // c7 | P
}
return nextGen; // c8 | 1
}T(P, T, L) = 1 + (1 * P^2) + (1 * P^2) + (P + 1) + (P * T * L^2) + (P * TL) + P + 1 = (2 * P^2)
We can see which methods need their time complexities to be calculated, so let's get started.
public Population GetRandomPopulation() {
List<Population> sortedPopulations = Populations.OrderBy(x => x.Fitness).ToList(); // c1 | P^2
double random = new Random().NextDouble(); // c2 | 1
int index = 0; // c3 | 1
Population currPopulation = sortedPopulations[0]; // c4 | 1
while (currPopulation.NormalizedFitness > 1 - random) { // c5 | P + 1
random -= currPopulation.NormalizedFitness; // c6 | P
currPopulation = sortedPopulations[++index]; // c7 | P
}
return currPopulation; // c8 | 1
}It's time complexity would be:
T(P) = P^2 + 1 + 1 + 1 + P + P + P + 1 = P^2 + 3P = P^2 = O(P^2)
private Population Crossover(Population A, Population B) {
List<List<int>> orders = new(); // c1 | 1
Random random = new(); // c2 | 1
for (int k = 0; k < A.Paths.Count; k++) { // c3 | T + 1
int start = random.Next(0, A.Paths[k].Count); // c4 | T
int end = random.Next(start + 1, B.Paths[k].Count); // c5 | T
List<int> order = A.Paths[k].GetRange(start, end - start); // c6 | T
int left = Program.Places.Count - order.Count; // c7 | T
for (int i = 0; i < left; i++) { // c8 | T * (L + 1)
for (int j = 0; j < B.Paths[k].Count; j++) { // c9 | TL * (L + 1)
if (!order.Contains(B.Paths[k][j])) { // c10 | T * L^2
order.Add(B.Paths[k][j]); // c11 | T * L^2
}
}
}
// Make sure 0 is always the first element
order.Remove(0); // c12 | T
order.Insert(0, 0); // c13 | T
orders.Add(order); // c14 | T
}
return new Population(orders); // c15 | 1 * TL
}T(T, L) = 1 + 1 + (T + 1) + T + T + T + T + (T * (L + 1)) + (TL * (L + 1)) + (T * L^2) + (T * L^2) + T + T + T + TL = 8T + 2TL + 3(T * L^2) = T * L^2 = O(T * L^2)
public Population(List<List<int>> paths) {
Paths = paths; // c1 | 1
Fitness = GetFitness(); // c2 | TL
Price = GetPrice(); // c3 | TL
}T(T, L) = 1 + TL + TL = 2TL = O(TL)
private double GetDistance(List<int> order) {
double distance = 0; // c1 | 1
for (int i = 0; i < order.Count - 1; i++) { // c2 | L + 1
Location place1 = Program.Places[order[i]]; // c3 | L
Location place2 = Program.Places[order[i + 1]]; // c4 | L
// Convert to km
double xDiff = (place1.X - place2.X) / 1000; // c5 | L
double yDiff = (place1.Y - place2.Y) / 1000; // c6 | L
distance += Math.Sqrt(Math.Pow(xDiff, 2) + Math.Pow(yDiff, 2)); // c7 | L
}
return distance; // c8 | 1
}T(L) = 1 + (L + 1) + L + L + L + L + L + 1 = 6L = L = O(L)
private double GetFitness() {
List<double> times = new(); // c1 | 1
// 1 km = 1 min
Paths.ForEach(order => times.Add(GetDistance(order))); // c2 | T * L
// Adding rest times
for (int i = 0; i < times.Count; i++) { // c3 | T + 1
int timeInPlaces = Paths[i].Count * 60; // c4 | T
int restTimes = (int)Math.Floor(times[i] / 16); // c5 | T
times[i] = times[i] + restTimes * 8 * 60 + timeInPlaces; // c6 | T
}
return times.Max(); // c7 | T
}T(T, L) = 1 + TL + (T + 1) + T + T + T + T = TL + 5T = TL = O(TL)
private double GetPrice() {
List<double> distances = new(); // c1 | 1
Paths.ForEach(order => distances.Add(GetDistance(order))); // c2 | T * L
double price = 0; // c3 | 1
distances.ForEach(d => price += Math.Sqrt(d)); // c4 | T
return price; // c5 | 1
}T(T, L) = 1 + TL + 1 + T + 1 = TL + T = TL = O(TL)
private Population Mutate(Population population) {
List<List<int>> orders = new(); // c1 | 1
for (int i = 0; i < population.Paths.Count; i++) { // c2 | T + 1
List<int> shuffledOrder = population.Paths[i].Shuffle(); // c3 | T * T
// Make sure 0 is always the first element
shuffledOrder.Remove(0); // c4 | T
shuffledOrder.Insert(0, 0); // c5 | T
orders.Add(shuffledOrder); // c6 | T
}
return new Population(orders); // c7 | TL
}T(T, L) = 1 + (T + 1) + T^2 + T + T + T + TL = 4T + T^2 + TL = TL = O(TL)
There most likely will be more locations than travellers, hence O(TL) and not O(T^2).
Now we can move onto the next main method - NormalizeFitnesses
public void NormalizeFitnesses() {
double sum = 0; // c1 | 1
Populations.ForEach(gen => sum += gen.Fitness); // c2 | P
for (int i = 0; i < Populations.Count; i++) { // c3 | P + 1
Populations[i].SetNormalizeFitness(sum); // c4 | P
}
}T(P) = 1 + P + (P + 1) + P = 3P = P = O(P)
And this is the last method we need to calculate time complexity for. Then we will be able to find whole algorithm's time complexity.
private void CheckBestPopulation(Generation generation) {
bestGenPopulation = generation.GetBestPopulation(); // c1 | P
if (bestGenPopulation < bestPopulation!) { // c2 | 1
bestPopulation = bestGenPopulation; // c3 | 1
OnBestSolutionChanged?.Invoke(bestPopulation);
}
}T(P) = P + 1 + 1 = P = O(P)
public static List<T> Shuffle<T>(this List<T> list) {
List<T> temp = new(list); // c1 | 1
Random random = new(); // c2 | 1
int n = temp.Count; // c3 | 1
while (n > 1) { // c4 | n + 1
n--; // c5 | n
int k = random.Next(n + 1); // c6 | n
(temp[n], temp[k]) = (temp[k], temp[n]); // c7 | n
}
return temp; // c8 | 1
}T(n) = 1 + 1 + 1 + (n + 1) + n + n + n + 1 = 4n = n = O(n)
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