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Obtaining solutions through classical optimisation; Obtaining solutions by a careful study of problem structure; Obtaining solutions by the use of heuristics or meta-heuristics (global optimisation); Obtaining solutions by applying game theory (multi-objective optimisation); Obtaining solutions through soft computing-based optimisation.
Computers are widely used in obtaining solutions.
We present a perturbation theory for obtaining solutions to Hamiltonian dynamical systems.
Monte Carlo methods have become popular for obtaining solutions to global optimization problems.
Genetic algorithms have gained popularity as effective search procedures for obtaining solutions to traditionally difficult space mission optimization problems.
These techniques are used for developing structural optimization algorithms that are particularly effective for obtaining solutions to discrete programming problems.
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The obtained solutions are also presented graphically.
Examples are given to illustrate the performance, plausibility and accuracy of the obtained solutions.
The obtained solutions form what is called a "Pareto front".
Finally, obtained solutions were mixed, filtrated and packed.
We make the comparison between obtained solutions with each of them and also with obtained solutions with different methods.
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