PWR fuel management optimization using continuous particle swarm intelligence

Pattern optimization of PWR reactor using hybrid parallel Artificial Bee Colony

“…The convergence behavior can also be governed by inertia weight. In order to allowing the algorithm to exploit some specific areas over the iterations, the inertia weight w is updated according to the following equation (Khoshahval et al, 2010) w where w max , w min are the greatest and smallest values of inertia weight, t max and t are the maximum and current iteration number, respectively. Furthermore, roulette wheel mechanism is used to model onlooker bee recruiting.…”

“…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;).…”

Optimization of refueling cycle length by an enhanced PSO with novel mutation operator

“…Several metaheuristics algorithm developed successfully have been extended to solve the ICFMO, such as harmony search (HS) (Nazari et al, 2013), Genetic Algorithm (GA) (Norouzi et al, 2011), Ant Colony Optimization (ACO) and Particle Swarm Optimization (POS) (Meneses et al, 2009;Khoshahval et al, 2010).…”

Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

“…The tour will find the best position among all the possible solutions and this is referred to as the "global best position" (g best ). Some features of the continuous particle swarm optimization are found in the literature [38][39][40]. In the literature survey, PSO in its standard form has been widely used for unconstrained optimization projects.…”

“…At the end of each iteration of the PSO the global best that is found is adopted as the initial solution by the LSA. The Flow chart of CPSO is shown in Figure 2, and the pseudocode of the proposed algorithm (CPSO) is also given above in algorithm 1 [39][40][41]. …”