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. | (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 |
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