A hybrid method for in-core optimization of pressurized water reactor reload core design

Application of the Distributed Genetic Algorithm for In-Core Fuel Optimization Problems under Parallel Computational Environment.

“…(4) Repeat (2) and (3) until improvement on the fitness value of the best loading pattern becomes smaller among successive several generations. In other words, repeat (2) and (3) until the GA search converges. Though GA is simple and effective optimization method, it has some weaknesses.…”

“…The optimization parameters in Table 3 are set based on the experiences in the previous studies. [1][2][3][4][5][6][7][8] Total number of evaluate candidates (6,000) is not enough to obtain a "true optimum" solution, but it was sufficient to provide valuable solutions in actual in-core fuel management tasks.…”

“…Recently, some stochastic optimization methods such as the simulated annealing, genetic algorithms (GA) and evolutionary algorithms are successfully applied to the practical in-core optimization problems [1][2][3][4][5][6][7][8] and are used as practical design tools. [9][10][11] However, since the stochastic optimization methods typically require several thousands evaluations of loading patterns to obtain a converged solution, computation time tends to be longer.…”

Pattern optimization of PWR reactor using hybrid parallel Artificial Bee Colony

“…possible solution for n FAs, without considering the placement of burnable poison and orientations of these FAs which indeed increases more the complexity of the problem. Furthermore, these LPs are not feasible solutions in accordance with safety constraints, causing the feasible regions to be disconnected (Stevens, 1995).…”

“…Several algorithms have been developed and successfully applied to optimize reactor core loading problem such as Dynamic Programming (Wall and Fenech, 1965), direct search (Stout, 1973), Variational Techniques (Terney and Williamson, 1982), Backward Diffusion Calculation (Chao et al, 1986), Reverse Depletion (Downar and Kim, 1986;Kim et al, 1987), Linear Programming (Stillman et al, 1989), Simulated Annealing (Stevens, 1995), Ant Colony algorithm (Schirru et al, 2006), Safarzadeh et al (2011) applied ABC algorithm to power flattening of PWR reactor, continuous Genetic Algorithm (GA) introduced for flatting power distribution (Zolfaghari et al, 2009;Norouzi et al, 2011), discrete PSO (Babazadeh et al, 2009), continuous PSO (Khoshahval et al, 2010), Mohseni et al used GA in multi-objective optimization of lowering power peaking factor, maximization of the effective multiplication factor (Mohseni et al, 2008), Cellular Automata for maximizing initial excess reactivity and minimizing power peaking factor , Perturbation Theory (Stacey, 1974;Hosseini and Vosoughi, 2012), ArtificialIntelligence techniques like Artificial Neural Networks (ANNs) (Sadighi et al, 2002), and combination of fuzzy logic and ANN (Kim et al, 1993) are the ones most commonly used in core fuel management. A further study based on hybrid algorithms was performed (Stevens, 1995;Erdog and Geçkinli, 2003;).…”