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VNS searches the solution space by systematically changing the neighborhoods.
PSO searches the solution space using particles that are updated in each iteration.
Our approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating potential monitoring designs.
The approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating the incumbent monitoring design.
Thus, the algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches.
In this manner, the algorithm searches the solution space in a random way and at the same time it avoids becoming stuck at local minima.
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A large value of J indicates fast convergence, and the genetic algorithm searches the solutions that indicate large fitness values.
Then, the GA searched the solution space and determined the optimized weight of each feature.
In ANS, a group of individuals collaboratively search the solution space for an optimal solution of the optimization problem considered.
Finally, we design a program to search the solution and use the results to design multilayer radar absorbing materials.
Exhaustively searching the solution space by OPUS10 can yield an optimal BOM configuration; however, it may be formidably time-consuming.
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