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Exhaustively searching the solution space by OPUS10 can yield an optimal BOM configuration; however, it may be formidably time-consuming.
These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space.
We propose a genetic algorithm for searching the solution space, which uses a new placement heuristic for decoding the gene based on the best fit heuristic designed for the strip packing problems.
Since the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely searching the solution space and finding optimal solutions are proposed.
However, it is not necessary to solve each (mathrm{P2} upsilon)) associated with each (upsilonin B^{theta}) for searching the solution of problem (P), that is, by using the following proposition we can obtain an improvement of the algorithm.
Furthermore, by avoiding searching the solution space which is computational complex, our iterative methods are easy to compute by adding up some correlation terms with different weights in (24).
Similar(47)
VNS searches the solution space by systematically changing the neighborhoods.
Then, the GA searched the solution space and determined the optimized weight of each feature.
PSO searches the solution space using particles that are updated in each iteration.
In ANS, a group of individuals collaboratively search the solution space for an optimal solution of the optimization problem considered.
The approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating the incumbent monitoring design.
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