Sentence examples for evolution of objective from inspiring English sources

In order to ensure accurate performance of the ACO algorithm, evolution of objective function (Eq. 8) versus iteration number is depicted in Fig. 10 for water injection tests.

Evolution of the objective functional of the SD and GN methods (bottom panel).

Figure 8 shows the evolution of the objective value for an algorithm test of the NMPC over a simulated period of 100 days.

Figure4 shows evolution of the objective ∑ i = 1 J U i ( r ̄ i ( t ) ) and the dual function value g(t):=g(λ(t),μ(t)).

The evolution of the objective functions shows the importance of the tool preform shape optimization for the forging quality and energy saving.

In Figs. 1 and 2, we plot the evolution of the objective function versus the number of iterations when solving Example 1 with Algorithm 1 and the smoothing gradient method respectively.

Fig. 13 shows the evolution of the objective function and non-linear constraint during optimization.

Convergence curves plot the evolution of the objective function, as defined in Equation (1), as a function of the number of evaluations or the computation time (since the overhead is different for each method).

The evolution of the objective function with the number of source nodes in the permissible region is illustrated in Fig. 4. Figure 4(a) shows the objective function f =min ∑ λ ‖ G ˜ (d, R ; λ ) s (R ) − φ ˜ (d ; λ ) ‖ 1 as a function of the number of source nodes in the permissible source region.

To better observe the convergence and performance of our algorithm, we plot the evolutions of the objective function value in Figure 1, (mathit{rms}_{S}) in Figure 2 and (mathit{rms}_{L}) in Figure 3, respectively.

Figure 2 Evolution of the average objective in the GA for diverging and translating tree sequences.