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A robust optimization formulation seeks for optimal solutions that optimize the objective function and meet the problem requirements for all possible uncertainties in constraint coefficients.
The genetic algorithm (GA) and a related procedure called the grouping genetic algorithm (GGA) are solution methodologies used to search for optimal solutions in constrained optimization problems.
Most applications of evolutionary optimization are concerned with the search for optimal solutions satisfying pre-defined constraints while minimizing or maximizing desired goals.
The proposed synthesis method was based on the overlapped Gaussian-apodization method and the evolutionary optimization algorithm which can efficiently search for optimal solutions and simultaneously take into account various experimental requirements for the fabrication of the designed filters.
Although the DP algorithm allowed optimization of the NP-complete problem into pseudo-polynomial time, the search for optimal solutions can still be completed in exponential time complexity in the worst-case scenario.
When searching for optimal solutions it is sufficient to traverse normal forms of flip operations only.
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However, improvement of some process variables may deteriorate some other criteria due to conflicting regions of factor interests for optimal solution in multi-objective optimization (MOO) processes.
Also, a theorem for optimal solution existence is proposed.
This allows more flexibility by tunning up both parameters for optimal solution.
The problem is first formulated into a linear programming model to search for optimal solution.
A methodology for optimal solution is proposed based on objective function and is given in Fig. 4.
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