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Therefore, this study aimed to develop an integrated multi-objective optimization model for determining the optimal solution in the STE system.
In the optimization problem, we seek an optimal solution in the sense that the index of crystalline defects is minimized while the single-crystal growth rate is maximized.
Hence, in this paper, Artificial Bee Colony (ABC) optimization is deployed for finding the near optimal solution in the discrete space.
Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization.
PSO is a global optimization method, it does not need any prior information when it seeks the optimal solution in the solution space.
The optimization method features: (1) employing the block coordinate descent method to find the optimal solution in the space of enrichment difference parameters, (2) using scoping libraries to reduce the amount of CASMO-4 calculation, and (3) integrating a core critical constraint into the objective function that is used to quantify the quality of an axial enrichment design.
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Both parties should be willing to accept a slightly sub-optimal solution in the interest of time.
The projection of a Pareto-optimal solution in the objective space is called a Pareto point.
The DCA is fast and furnished an optimal solution in almost all cases, and a near-optimal solution in the remaining cases.
Simulated annealing is usually used in neural networks to allow the network to escape suboptimal basins of attraction in order to converge to a near-optimal solution in the solution space.
In neural networks, this process is imitated to ensure that the neural network escapes less optimal local minima to converge to a near-optimal solution in the solution space.
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