PSO Based Optimization of Testing and Maintenance Cost in NPPs

Algorithm 2

MOBPSO.

(1) Initialize a population array of particles with random positions and velocities on

        dimensions in the search space and the population size is = Pop_Max.

(2) For = 1 to

        Evaluate the fitness function in dimensional variables, namely,

         = .

        End for

(3) Divide the initial population into two subsets P_Set and NP_Set. whose population sizes are

          and respectively.

(4) Update the velocity and position of each particle according to (15)-(16).

        Where is selected from the subset of P_Set randomly. For the constraint-handling

        approach, update the with if is in the constraint interval,

        namely is feasible.

(5) Dynamic switching strategy: Compare each particle in NP_Set with that in P_Set. Let the

        particles in NP_Set be and the elements in P_Set be .

        For = 1 to

              For = 1 to

                    If ) <

                    Switch and then update their index and position in sets.

                    End if

              End for

        End for

        Update the two sets P_Set and NP_Set.

        If there exist same particles in P_Set, delete them and re-initialize particles in NP_Set,

        and vice versa.

        Update and .

        If or not reaching the given maximum iterative number, goto Step  3.