| (1) Initialize the positions of particles with randomly generated binary values, and set velocities of particles to 0, the |
| generation number , population size . |
| (2) initialized Binary Population Set |
| (3) |
| (4) while Stopping criteria is not reached do |
| (5) For each particle in do |
| (6) Compute the fuzziness fitness functions and using the binary valued positions. |
| (7) end for |
| (8) Compare the particle’s current finesses and with particle’s best and . |
| (9) if then |
| (10) |
| (11) end if |
| (12) if then |
| (13) |
| (14) end if |
| (15) Compare the global best fitness value with the population’s overall best values for the first fitness function, . |
| (16) if then |
| (17) |
| (18) end if |
| (19) Compare the global best fitness value with the population’s overall best values for the second fitness function, . |
| (20) if then |
| (21) |
| (22) end if |
| (23) Update the velocities of particles using Eq. (5) |
| (24) Update the positions of particles using Eq. (6) |
| (25) Insert the updated particles into the |
| (26) Create the binary change-detection mask using Eq. (7) |
| (27) |
| (28) Use the Ranking and crowding distance operator [27] |
| (29) Select Pareto solutions or non-dominated solutions |
| (30) Insert selected Pareto solutions into the |
| (31) |
| (32) end while |
| (33) Output1: Set of Pareto optimal solutions |
| (34) Output2: Set of Binary change detection masks |
