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Non-linear and complicated optimization problems which cannot be solved employing conventional optimization algorithms such as linear programming can be effectively solved using genetic algorithms.
This characteristic is referred to as the block sparsity or group sparsity [19], which can be effectively solved using techniques that take such property into account.
This study provides a basis for advancing the size and scope of water resources problems that can be effectively solved using MOEAs.
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The complex nonlinear optimization problem presented by the wind farm model is effectively solved using constrained Particle Swarm Optimization (PSO).
Both these problems are effectively solved using RCFSTC in the OGS, as the model with RCFSTC is not blocking any access in OGS, stiffness is uniformly distributed over the whole base of the structure and RC column section is also not changing at the junction.
The control design problem is transformed into the solvability of a set of strict LMIs, which can be effectively solved by using interior-point algorithms.
Subsequently, the explicit characterization of the resilient control gain is obtained by means of linear matrix inequalities that can be effectively solved by using the ideas of convex optimization.
The resulting optimization problem involving a combinatorial and nonlinear search space cannot be effectively solved for the global solution using conventional optimization techniques.
For the first sub-subproblem, we transform it into concave function by using the nonlinear fractional programming, which can be effectively solved by the Dinkelbach method [22].
In this case, the problem originating from the use of continuous conducting floating gates can be effectively solved.
The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm.
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