1. ACO - TSP for Example

2. User can use methods Init() - polymorphism –Init(int PopulationSize, int VariableDimension, double VariableLowerbound, double VariableUpperbound, string RepeatableOption, string CycleOption) –Init(int PopulationSize, int VariableDimension, double[] VariableLowerbound, double[] VariableUpperbound, string RepeatableOption, string CycleOption) Run() - polymorphism –Run(int Iteration) –Run(int Iteration, double Alpha, double Beta, double GlobalPersisitenceRate, double GlobalConstant) –Run(int Iteration, double Alpha, double Beta, double LocalPersistenceRate, double LocalConstant, double GlobalPersistenceRate, double GlobalConstant) GenerateAntPath() - polymorphism –GenerateAntPath(double Alpha, double Beta) –GenerateAntPath(double Alpha, double Beta, double LocalPersistenceRate, double LocalConstant) GlobalPheromoneUpdate() –GlobalPheromoneUpdate(double GlobalPersistenceRate, double GlobalConstant)

3. User can override methods Method –public override double InitVisibility() –public override double Fitness(int[] solution)

4. ACOOption Class User used ACOOption when having no idea to determine problem options or strategy options. ACOOption –RepeatableOption Repeatable, Nonrepeatable –CycleOption Cycle, Noncycle.

5. ACOOption Class ACOOption.RepeatableOption.Repeatable ACOOption.RepeatableOption.Nonrepeatable ACOOption.CycleOption.Cycle ACOOption.CycleOption.Noncycle

6. Step 1 and 2 using System; using System.Collections.Generic; using System.Text; using System.IO; using Metaheuristic; namespace Testing { class TSP : ACO { } Step 1: Include Metaheuristic. Step2: Problem class must inherit PSO.

7. Step 3 using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { } public void Read() { StreamReader sr = new StreamReader(@” tsp01.txt”); string line = sr.ReadToEnd(); string[] AllLine = line.Split(',', '\n'); for (int i = 0; i < distance.GetLength(0); i++) for (int j = 0; j < distance.GetLength(1); j++) distance[i, j] = double.Parse(AllLine[i * (distance.GetLength(1)) + j]); sr.Close(); } To read city distance.

8. Step 4 and 5 using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); } public void Read()… public override void InitVisibility() { for (int i = 0; i < Visibility.GetLength(0); i++) for (int j = 0; j < Visibility.GetLength(1); j++) Visibility[i, j] = 1 / distance[i, j]; } Step 3: Creating object. Step 4: To override InitVisibility.

9. Step 6 using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution) { double sum = 0; for (int j = 0; j < solution.GetLength(0) - 1; j++) sum = sum + distance[solution[j], solution[j + 1]]; sum = sum + distance[solution[solution.GetLength(0) - 1], solution[0]]; return sum; } To override Fitness funcitoin.

10. Step 7 using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; double[] Low = new double[10] { 0, 0, …, 0 }; double[] Up = new double[10] { 9, 9, …, 9 }; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, “Cycle”); //ant.Init(100, 10, Low, Up, “Nonrepeatable”, ACOOption.RepeatableOption.Cycle); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… } User can use arrays to input lower bound and upper bound for each variable. RepeatableOption and CycleOption have two expressions of ACOOption and string. Polymorphism

11. Step 8 – Simple Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, “Cycle”); ant.Run(1000); //ant.Run(1000, 1, 5, 0.9, 1); //ant.Run(1000, 1, 5, 0.9, 0.001, 0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… } Polymorphism

12. Step 8 – Advanced Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, “Cycle”); for (int iter = 1; iter <= 1000; iter++) { ant.GenerateAntPath(1, 5); //ant.GenerateAntPath(1, 5, 0.9, 0.001); ant.GlobalPheromoneUpdate(0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… } Polymorphism

13. User requirement 1 for TSP User requirement. –PopulationSize = 100, VariableDimension = 10, VariableLowerbound = 0, VariableUpperbound = 9, RepeatableOption = “Nonrepeatable”, CycleOption = “Cycle”. –Iteration = 1000. –State transition rule Alpha = 1, Beta = 5 –Global pheromone update. GlobalPersisitenceRate = 0.9, GlobalConstant = 1. We will give two examples to satisfy him if user has requirement above. User can access public attributes for Gbest (the best solution for global) and GbestFitness (the best fitness for global).

14. Example 1 - Simple Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, ACOOption.RepeatableOption.Cycle ); ant.Run(1000, 1, 5, 0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… }

15. Example 2 - Advanced Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, ACOOption.RepeatableOption.Cycle); for (int iter = 1; iter <= 1000; iter++) { ant.GenerateAntPath(1, 5); ant.GlobalPheromoneUpdate(0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… }

16. User requirement 2 for TSP User requirement. –PopulationSize = 100, VariableDimension = 10, VariableLowerbound = 0, VariableUpperbound = 9, RepeatableOption = “Nonrepeatable”, CycleOption = “Cycle”. –Iteration = 1000. –State transition rule Alpha = 1, Beta = 5 –Local pheromone update. LocalPersisitenceRate = 0.9, LocalConstant = 0.001. –Global pheromone update. GlobalPersisitenceRate = 0.9, GlobalConstant = 1. We will give two examples to satisfy him if user has requirement above. User can access public attributes for Gbest (the best solution for global) and GbestFitness (the best fitness for global).

17. Example 1 - Simple Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, ACOOption.RepeatableOption.Cycle ); ant.Run(1000, 1, 5, 0.9, 0.001, 0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… }

18. Example 2 - Advanced Call using … namespace Testing { class TSP : ACO { double[,] distance = new double[10, 10]; static void Main(string[] args) { TSP ant = new TSP(); ant.Init(100, 10, 0, 9, ACOOption.RepeatableOption.Nonrepeatable, ACOOption.RepeatableOption.Cycle); for (int iter = 1; iter <= 1000; iter++) { ant.GenerateAntPath(1, 5, 0.9, 0.001); ant.GlobalPheromoneUpdate(0.9, 1); } public void Read()… public override void InitVisibility()… public override double Fitness(int[] solution)… }