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Open image in new window Fig. 11 The sensitivity analysis of changing ρ d by the objective value.
Open image in new window Fig. 12 The sensitivity analysis of changing ρ t by the objective value.
Open image in new window Fig. 8 The sensitivity analysis of changing probability of disturbance by objective value Open image in new window Fig. 9 The sensitivity analysis of changing probability of disturbance by the objective value Open image in new window Fig. 10 The sensitivity analysis of changing probability of disturbance by the objective value.
Motivation is generated by the objective value of the resource (Maynard Smith 1982; Enquist 1985; Barlow et al. 1986; Enquist and Leimar 1987; Riechert 1998; Hurd 2006; Baker and Maner 2008; Bergman et al. 2010) as well as by the level of deficiency (Nosil 2002; Chancellor and Isbell 2008), i.e., the positive or negative experience with the factor.
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For the proposed method, we first obtain a local minimizer by Algorithm 1, where the objective value is -21.9350 dB.
Well, that's determined by the envelope theorem, according to which the change in the objective value is given by the partial derivatives of the objective function with respect to the exogenous parameters.
ACS, RACS, NN, GI 3, GA The objective value returned by the included algorithms.
The application of a scheduling heuristic (mathcal {H}) to a number of training instances (static instances or simulation replications) (T = {1,2,ldots,|T|}) results in performance measures (z_i(mathcal {H})), the objective value reached by the heuristic on instance i.
As a result of the primal projection in (12), now with the objective value modified by the weighting matrix W= [1/a 1,…,1/a J ] T, i.e., W 1 / 2 | | y k − y ̂ k | | 2, the corrected ŷ j k values can be expressed as ŷ j k = a j μ k − α + b j − a j K j ∈ A k m j j ∈ I k d j − a j K j ∈ S k (27).
The upper bound and lower bound of the objective value are obtained by solving the pair of geometric programs.
Figure3 shows the normalized power accuracy |p i - p⋆| / p⋆, where p i is the objective value at i th iteration, and p⋆ is the optimal objective value obtained by using centralized algorithm[16], Section IV.
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