A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Algorithm 3

A Chaotic Starling PSO Algorithm.

Input:  

   Particle Swarm Population Initialization

   for to (Population Size)

      Particle().Best.Position=Particle().Position;

      Particle().Best.Fitness=Particle().Fitness;

      if Particle().Best.Fitness<Best Particle. Fitness

      Best Particle.Position=Particle().Best.Position;

      Best Particle.Fitness=Particle().Best.Fitness;

      End if

     End for

     for to (Max Iteration)

     Algorithm  1: The Standard PSO Algorithm

     (See Algorithm 1)

     Particle Selection

     = Euclidean Distance Function (Best Particle.Position);

     = Fitness Function (Particle().Fitness);

     Algorithm 2: Starling Birds  Collective Responses

      for to

      Find the neighbors in the neighborhood of particle using FER.

      The new position of particle : ;

      The new position of particle : ;

      if Particle().Fitness<Particle().Fitness

      Particle().Fitness=Particle().Fitness;

      Particle().Position=Particle().Position;

      Particle().Velocity=Particle().Velocity;

      if Particle().Fitness<Best Particle.Fitness

        Best Particle.Position=Particle().Best.Position;

        Best Particle.Fitness=Particle().Best.Fitness;

      End if

    End if

   End for

   ;

     End for

Output: Best Particle.Fitness