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The results obtained by NSGA-II are compared and validated with classical multi-objective approach based on weighted sum method using Differential Evolution (DE) algorithm.
For example, [30] introduced a type of dynamic clustering algorithm based on weighted sum rate maximization for the downlink of a MU-MIMO CoMP system.
In terms of receiver design, an optimal receiver design based on weighted sum detectors was proposed in [19], and a ligand-binding reception model was studied in [20].
Algorithm 1 Deriving ( ⪯ C u, ⪯ C ∖ { 0 } u, ⪯ int C u ) -robust solutions to ( P ( U ) ) based on weighted sum scalarization: Input: Uncertain multi-objective problem P ( U ), solution sets Opt C = Opt C ∖ { 0 } = Opt int C = ∅.
Algorithm 2 Deriving a single accepted ( ⪯ C u, ⪯ C ∖ { 0 } u, ⪯ int C u ) -robust solution to ( P ( U ) ) based on weighted sum scalarization: Input: Uncertain vector-valued problem ( P ( U ) ). Step 1: Choose a nonempty set C ¯ ⊂ C ∗ ∖ { 0 }.
The total score of each site is calculated based on weighted sum of the scores generated from Step 3. A site with a higher score would be deemed as a more favorable site based on the defined factors.
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The site-based score is based on the weighted sum of sites with dN/dS >1 normalized by the sequence length.
ADWMF is based on weighted median filter.
Statistical analyses were based on weighted data.
Analyses are based on weighted sample.
aPercentages are based on weighted population data.
More suggestions(17)
based on linear sum
based on weighted sample
based on weighted processing
based on weighted channel
based on weighted conversion
based on weighted congestion
based on weighted matching
based on weighted class
based on weighted graph
based on weighted vector
based on weighted windowed
based on weighted support
based on weighted averaging
based on weighted association
based on weighted regression
based on weighted condition
based on weighted conflict
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