public Particle(ProblemInstance problem, Evaluator evaluator)
{
this.problem = problem;
this.evaluator = evaluator;
position = DataPack.parseDataPack(problem, SolutionUtils.getRandomSolution(problem));
velocity = DataPack.parseDataPack(problem, SolutionUtils.getRandomSolution(problem));
p_best = new DataPack(position);
p_best_value = p_best.getValue(problem, evaluator);
}
public Solution minimize(ProblemInstance problem, Evaluator evaluator)
{
List <DataPack> chrs = new List <DataPack>();
for (int i = 0; i < population_size; i++)
{
chrs.Add(DataPack.parseDataPack(problem, SolutionUtils.getRandomSolution(problem)));
}
Optimizer optimizer = new ImprovedGreedyOptimizer();
chrs.Add(DataPack.parseDataPack(problem, optimizer.minimize(problem, evaluator)));
optimizer = new ScannerOptimizer();
chrs.Add(DataPack.parseDataPack(problem, optimizer.minimize(problem, evaluator)));
sort(chrs, problem, evaluator);
double last_value = -1, g_best_value;
for (int i = 0; i < iteration_times; i++)
{
for (int j = 0; j < population_size; j++)
{
DataPack dataPack = chrs[j];
if (MathUtils.probability(cross_over_rate))
{
chrs.Add(crossover(dataPack, chrs[MathUtils.u(0, chrs.Count)]));
}
else
{
chrs.Add(mutation(dataPack));
}
}
sort(chrs, problem, evaluator);
chrs = chrs.GetRange(0, population_size);
g_best_value = chrs[0].getValue(problem, evaluator);
if (last_value != g_best_value)
{
last_value = g_best_value;
Console.WriteLine("OPT = " + last_value.ToString());
}
}
sort(chrs, problem, evaluator);
Solution solution = chrs[0].getSolution(problem);
solution.tag = "遗传算法";
return(solution);
}
public Solution minimize(ProblemInstance problem, Evaluator evaluator)
{
DataPack g_best = DataPack.parseDataPack(problem, SolutionUtils.getRandomSolution(problem));
double g_best_value = g_best.getValue(problem, evaluator);
double last_value = -1;
List <Particle> particles = new List <Particle>();
for (int i = 0; i < population_size; i++)
{
particles.Add(new Particle(problem, evaluator));
}
Optimizer optimizer = new ImprovedGreedyOptimizer();
particles.Add(new Particle(problem, evaluator, optimizer.minimize(problem, evaluator)));
optimizer = new ScannerOptimizer();
particles.Add(new Particle(problem, evaluator, optimizer.minimize(problem, evaluator)));
for (int i = 0; i < iteration_times; i++)
{
foreach (Particle particle in particles)
{
particle.update(ref g_best, ref g_best_value, particles);
}
if (last_value != g_best_value)
{
last_value = g_best_value;
Console.WriteLine("OPT = " + last_value.ToString());
}
}
Solution solution = g_best.getSolution(problem);
solution.tag = "粒子群优化算法";
return(solution);
}