public void Decreasing_Minimize(List <Particle> particle)
{
//Initializing linear decreasing inertia
this.inertia.Initialize_LinearDecreasingInertia();
//for timestep=1
//Finding index of best particle
this.best_index = this.gbest.Return_gbest_Minimization(particle, this.numberOfParticles);
//calculating average fitness
this.average = this.averageFitness.Return_AverageFitness(particle, this.numberOfParticles);
//calculating population mean dispersion
this.meanDispersion = disp.calculateDispersion(particle);
//adding best particle fitness, average fitness of population and mean population dispersion to output class
this.output[this.run].Add_Values(1, this.average, particle[this.best_index].ReturnFitness(), this.meanDispersion);
//for rest of timesteps
for (int i = 2; i <= this.numberOfTimeSteps; i++)
{
//updating velocity and position
for (int j = 0; j < this.numberOfParticles; j++)
{
//every particle calculating its own personal best
particle[j].Calculate_Pbest_Minimize();
//updating velocity
particle[j].Update_LinearDecreasingVelocity(particle[this.best_index], this.inertia.wt);
//updating position
particle[j].Update_Position();
}
//updating inertia
this.inertia.Update_LinearDecreasingInertia(this.numberOfTimeSteps, i);
//calculating best particle
this.best_index = this.gbest.Return_gbest_Minimization(particle, this.numberOfParticles);
//calculating average fitness
this.average = this.averageFitness.Return_AverageFitness(particle, this.numberOfParticles);
//calculating population mean dispersion
this.meanDispersion = disp.calculateDispersion(particle);
//adding best particle fitness, average fitness of population and mean population dispersion to output class
this.output[this.run].Add_Values(i, this.average, particle[this.best_index].ReturnFitness(), this.meanDispersion);
}
}
//method that runs GA Algorithm
public List <Output> RunGA()
{
//clearing output list
this.output.Clear();
//initializing list of individuals
List <Individual> ind = new List <Individual>();
//initializing list of offsprings
List <Offspring> offspring = new List <Offspring>();
//variable that stores best individual fitness value
double best_fitness_in_generation;
//setting run to 0
this.run = 0;
while (this.run < this.number_runs)
{
//clear lists of individuals and offsrpings
ind.Clear();
offspring.Clear();
//initializing values in dispersion class
this.disp.InitializeDispersion(this.pop_size, this.uniqueGenesFound);
//add an instance of output class to list in each run
this.output.Add(new Output());
//initializing list of individuals
for (int i = 0; i < this.pop_size; i++)
{
//initialize individuals here
ind.Add(new Individual(this.uniqueGenesFound, this.number_genes, this.higher_range, this.lower_range, this.input_function, this.random));
}
//initializing list of offspring
for (int i = 0; i < this.offspring_size; i++)
{
offspring.Add(new Offspring(this.uniqueGenesFound, this.input_function, this.number_genes));
}
//running GA Algorithnm
//calculations for time step1
//calculating average fitness of population
this.average = average_fitness.return_averageFitness(ind, this.pop_size);
//calculating population mean dispersion
this.mean_dispersion = disp.calculateDispersion(ind);
//calculating population best fitness here
this.best_index = best_individual.return_best_individual(ind, this.pop_size, this.optimization_type);
best_fitness_in_generation = ind[this.best_index].CalculateFitness();
//adding generation1 values to output class
this.output[this.run].Add_Values(1, this.average, best_fitness_in_generation, this.mean_dispersion);
//for rest of generations
for (int i = 2; i <= this.number_generations; i++)
{
//performing mating selection
switch (this.selection_method)
{
case "Tournament":
offspring = this.mating.tournament_selection(ind, offspring, this.pop_size, this.offspring_size, this.tournament_size, this.number_genes, this.optimization_type, this.random);
break;
case "Fitness_Proportionate":
offspring = this.mating.fitness_proportionate(ind, offspring, this.pop_size, this.offspring_size, this.number_genes, this.optimization_type, random);
break;
case "Rank":
offspring = this.mating.rank_selection(ind, offspring, this.pop_size, this.offspring_size, this.number_genes, this.optimization_type, this.random, this.s);
break;
}
//performing recombination
switch (this.recombination_type)
{
case "One_point_crossover":
offspring = this.recomb.onepoint_crossover(offspring, this.offspring_size, this.crossover_rate, this.number_genes, this.random);
break;
case "Arithmentic_single_crossover":
offspring = this.recomb.arithmetic_single_recombination(offspring, this.offspring_size, this.crossover_rate, this.a, this.number_genes, this.random);
break;
case "Arithmetic_simple_crossover":
offspring = this.recomb.arithmetic_simple_recombination(offspring, this.offspring_size, this.crossover_rate, this.a, this.number_genes, this.random);
break;
case "Arithmetic_whole_crossover":
offspring = this.recomb.arithmetic_whole_recombination(offspring, this.offspring_size, this.crossover_rate, this.a, this.number_genes, this.random);
break;
}
//performing mutation
switch (this.mutation_type)
{
case "Normal_Distribution":
offspring = mutation.gaussian_mutaion(offspring, this.offspring_size, this.mutation_rate, this.number_genes, this.random, this.normalDist, this.lower_range, this.higher_range);
break;
case "Cauchy_Distribution":
offspring = mutation.cauchy_mutation(offspring, this.offspring_size, this.mutation_rate, this.number_genes, this.random, this.cauchyDist, this.lower_range, this.higher_range);
break;
}
//performing survivor selection
switch (this.survivor_selection_method)
{
case "Truncation":
ind = survivor.truncation_selection(ind, offspring, this.pop_size, this.offspring_size, this.number_genes, this.optimization_type, this.random);
break;
}
//calculating average fitness of population
this.average = average_fitness.return_averageFitness(ind, this.pop_size);
//calculating population mean dispersion
this.mean_dispersion = disp.calculateDispersion(ind);
//calculating population best fitness here
this.best_index = best_individual.return_best_individual(ind, this.pop_size, this.optimization_type);
best_fitness_in_generation = ind[this.best_index].CalculateFitness();
//adding generation1 values to output class
this.output[this.run].Add_Values(i, this.average, best_fitness_in_generation, this.mean_dispersion);
}
this.run = this.run + 1;
}
//returning instance of output class
return(this.output);
}
