Research Article

Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO

Algorithm 1

PSOMOF algorithm.
Initialize ()
Minimize σ ()
 For each particle generates the position and velocity randomly.
  Xp   the initial position // Xp is the particles best historical
  Xg   the initial position // Xg is the best of all particles
  Repeat
   Determine velocity using (12).
   Update new position particle using (11).
   Determine of new position using (10). If the new position has a lower and CR < 0.1 updated new position is allowed
   otherwise, update new position is canceled and keeping the current position.
   Choosing the new Xp and Xg based on value σ
  Until max iterations is reached
  Get minimal σ
Minimize CR ()
 For each particle generates the position and velocity randomly.
  Xp   the initial position // Xp is the particles best historical
  Xg   the initial position // Xg is the best of all particles
  Repeat
   Determine velocity using (12).
   Update new position particle using (11).
   Determine CR of new position using (4). If the new position has a lower CR and CR < 0.1 updated new position is allowed
   otherwise, update new position is canceled and keeping the current position.
   Choosing the new Xp and Xg based on value CR
  Until max iterations is reached
  Get minimal CR
Minimize CR-σ ()
  CRo 0.1
  Xp   the initial position // Xp is the particles best historical
  Xg   the initial position // Xg is the best of all particles
  While CRmin < CRo
   Repeat
    Determine velocity using (12).
    Update new position particle using (11).
    Determine CR of new position using (4). If the new position has a lower CR and CR < 0.1 updated new position is allowed
    otherwise, update new position is canceled and keeping the current position.
    Choosing the new Xp and Xg based on value CR
   Until max iterations is reached
   Store the modified matrix and its CR,
   CRo CRo − k // k is small value, in this study
  End While
  Get matrices with their CR,