The use of a MBLP model guaranteed the convergence with existing classical optimization methods and the optimal solution for the MTEP using commercial solvers.
The use of an MILP model guarantees convergence to optimality by using existing classical optimization methods.
As mentioned above, the problem of optimizing intelligent wells with classical optimization methods is widely addressed in the research papers found in the literature.
It is left for future work to design more sophisticated controllers that ensure optimal network performance by applying classical optimization techniques instead of simple balancing rules.
Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems.
Application of the classical optimization technique aided the formulation of an optimal strategy for minimization of power losses on transmission lines.
In the classical optimization, the 15% was found to be the optimized concentration, which agrees well with the previous studies, where 4 20% concentration was optimal (Houshmand et al. 2013; Kaur and Arora 2009; Sood et al. 2015).
To avoid the heuristic search during the feature selection procedure, MEMBAS optimizes a membership margin based objective function by using classical optimization techniques providing an analytical solution [ 60].
Hence gradient based classical optimization methods are not suitable for digital filter design due to sub-optimality problem.
The results are checked against a classical optimization algorithm.
It does not require the concavity/convexity, monotonicity or duality properties common to classical optimization problems.
