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On the other hand, the approximate optimal solution for each single-objective optimization problem is also obtained by solving the special case of problem (left ({widetilde {mbox{{P}}3}} right)).
For solving problem (P), we first give the following concept about an approximate optimal solution.
Thus, if (phi^{k}leqvarepsilon), then Algorithm 1 will stop and we obtain an approximate optimal solution of SO.
Moreover, the equivalence of the approximate optimal solution of optimal control problem and the solution of bounded robust control problem is also shown.
The paper explores the development of the modified back-propagation neural network (BPN) based meta-model that ensures the constraint feasibility of approximate optimal solution.
These estimates provide us with information about the quality of the approximate optimal solution obtained by applying the improvement control procedure.
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To solve the model, fuzzy simulation based tabu search algorithm is designed to seek approximate optimal solutions.
Approximate optimal solutions are found using a heuristic approach called simulated annealing combined with pure random search that generates efficient initial solutions.
We present a hybrid method for finding approximate optimal solutions for survivable network problems on complete graphs that takes advantage of k-tree solvability.
In order to solve the modeled 4PLRPF, a two-step genetic algorithm with the fuzzy simulation is designed to find approximate optimal solutions.
Finally, from the derived approximate optimal solutions, the robust resource allocation schemes which balance UL and DL power consumption are proposed.
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