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Next , 12test problems of different sizes are considered and solved by the proposed algorithms.

Then an LP model can be established and solved by the simplex method.

This approximation is formulated as an optimization problem and solved by the gradient descent algorithm.

Standard examples are selected and solved by the proposed method and error analyses are conducted.

The mathematical model is linearized and solved by the standard branch-and-bound technique.

The underlying optimization problems are transformed to unconstrained problems and solved by the Gauss Newton method.

The design optimization model is then constructed and solved by the sequential linear programming (SLP).

The structure is modelled and solved by the finite element method (FEM).

Finally, small and large-scale test problems are randomly generated and solved by the HSA algorithm.

The problem has been formulated using a linear integer programming model and solved by the CPLEX.

The stochastic differential equation was discretized and solved by the Euler-Maruyama method.

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