The tx-rx processing optimization with the criterion of minimizing the S-MSE was presented in [10-12] [10-12]e andhors of [13] proposed a weighthe S-MSE minimizauthorslgofithm by considering the channel gain as the weight factor.
It ((A_{text {OGC}}^{text {out}}(S_{{o}},S_{{b}}))) solves the ε 1-minimization problem by considering the arcs that limit the flow from the source to the sink and consequently minimizes the sum of the arcs pointing from object to background pixels (i.e., the outer cut) [11].
Power minimization is ensured by considering the minimization of the upper bound of the objective value.
In this study, we present a stochastic multi-objective formulation of the otherwise single objective groundwater optimization problem by considering minimization of prediction uncertainty as an additional objective.
NoC design flow contains many problems, one of which is called as application mapping problem, which is generally solved in the literature by considering minimization of the communication energy consumption only.
Wright and Mahar (2013) propose a centralized model for the NSP by considering minimization of costs and overtime, simultaneously.
First, a total power minimization problem is formulated by considering both power control of nodes and bit rate assignment.
Klarbring et al. (2003) and Liu and Kassab (2007) have further developed the cost function approach by considering minimization of the cost for the vascular tree as a whole in their formulations.
An optimization problem is proposed by considering the minimization of the decision cost.
By considering the minimization of Gain Sensitivity Product (GSP), the sixteen variables of objective function are reduced to eight variables which speed up the iteration process.
