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To deal with constraints, updating law is designed by using model predictive control scheme.
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The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied.
This process of relaxing the constraints and updating the design variables by using ILP is repeated until convergence.
For the optimization with non-negative constraints, our updating rule is based on the following two theorems.
Hence, the first constraint for updating the algorithm is proposed as Condition I: Prs(n)≥ζ and Pref(n)≥β.
Based on a comparison of essential figures of merit, namely vertex resolutions, event selection efficiencies, trigger performance and technical constraints, an updated baseline design is proposed.
To mitigate the impact of material delay on schedule performance, the developed optimization algorithm is re-run to generate an alternative schedule under the constraints of updated material supply status.
The application of the constraint for the two-dimensional case is illustrated in Figure6. Figure 6 Illustration of the separation constraint update for two feet in the horizontal plane.
Without these coupling constraint updates, the model was unable to predict accurate translocase levels (Additional file 3: Figure S2).
We also use a merit-function based line search in order to balance between decreasing the cost and keeping the constraints satisfied when updating the channel shape.
Finally, analyzing different speech characteristics of the reference and current frames, multiconditional updating constraints are proposed in order to obtain precise control on convergence characteristics.
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