A Novel Clustering Algorithm Inspired by Membrane Computing

Algorithm 1

Membrane clustering algorithm: a clustering algorithm based on tissue-like P systems.

Input parameters: Data set, , the number of clusters, , the number of cell, , the number of objects in each cell,

, maximum execution step number, , crossover rate, , and mutation rate, .

Output results: the optimal centers, .

Step  1. Initialization

 for    to  

  for    to   

   Generate th initial object for cell , ;

   Partition all data points into clusters, ;

   Compute the value of the object, ;

  end for

 end for

 Fill the global best object using the best of all initial objects;

 Set computing step ;

Step  2. Object evolution in cells

 for each cell () in parallel do

  Evolve all object () in its mating pool using evolution rules;

  Use truncation operation to maintain its best objects;

  for    to  

   Partition all data points into clusters, ;

   Compute the value of the object, ;

  end for

 end for

Step  3. Object communication between cells

 for each cell () in parallel do

  Transmit better objects in cell to its two adjacent cells;

  Receive better objects from its two adjacent cells into its mating pool;

  Update using the best object in cell ;

 end for

Step  4. Halt condition judgment

 if   is satisfied

  ;

  goto Step  2;

 end if

 The system exports the global best object in the environment and halts;