PSO Based Optimization of Testing and Maintenance Cost in NPPs

Algorithm 1

Basic PSO [18].

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

       dimensions in the search space.

(2) For loop

(3) For each particle, evaluate the desired optimization fitness function in variables.

(4) Compare particle’s fitness evaluation with its . If current value is better than , then

         set equal to the current value, and equal to the current location in -dimensional  space.

(5) Identify the particle in the neighborhood with the best success so far, and assign its index to

        the variable .

(6) Change the velocity and position of the particle according to (15)-(16).

(7) If a criterion is met (usually a sufficiently good fitness or a maximum number of iterations),

        exit loop.

(8) end loop