Research Article
PSO Based Optimization of Testing and Maintenance Cost in NPPs
(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. |
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