Adaptive weighting method for layout optimization with multiple priorities
View Patent ↗An adaptive weighting method for layout optimization differentiates different priorities by assigning the weight of a higher priority (p i ) to be multiple of the weight of a lower priority (p i−1 ) where W(p i )=m i % W(p i−1 . To avoid numerical imprecision, this method keeps the total cost in the objective function within a trustable range by scaling the initial weights in the objectives, while maintaining relativity, to produce the scaled weights.
1. A method of adaptive weighting for layout optimization of multiple objectives having priorities, comprising:
setting initial minimum perturbation weights for location perturbation and space perturbation, using a computerized device;
receiving, as input, priorities for said location perturbation, said space perturbation, and rules, and trade offs between said priorities;
calculating objective weights for said location perturbation, said space perturbation, and said rules based on said priorities and said trade offs using said computerized device;
modifying said objective weights to bias objectives of ones of said location perturbation, said space perturbation, and said rules having a same priority using said computerized device;
calculating an initial total objective cost based on said priorities, said trade offs and said minimum perturbation weights and said objective weights using said computerized device;
scaling said initial minimum perturbation weights and said objective weights to maintain subsequent total objective costs in a numeric range, while maintaining relativity, to produce scaled perturbation weights and scaled objective weights using said computerized device;
solving said layout optimization using said scaled perturbation weights and said scaled objective weights to produce a layout optimization using said computerized device; and
outputting said layout optimization.
2. The method according to claim 1 , wherein said scaling of said initial minimum perturbation weights and objective weights is performed using at least one different scaling method, including linear scaling, logarithmic scaling, and exponential scaling.
3. The method according to claim 1 , wherein said scaling is performed so as to maintain a defined amount of differentiation between different priorities.
4. The method according to claim 1 , wherein said priorities are within priority classes, and at least one of said priority classes has predefined scaling values.
5. The method according to claim 1 , further comprising considering a count, comprising a number of times that an element appears, to determine said subsequent total objective costs, wherein said count is normalized if said number of times that said element appears is greater than one.
6. A method of adaptive weighting for layout optimization of multiple objectives having priorities, comprising:
setting initial minimum perturbation weights for location perturbation and space perturbation, using a computerized device;
receiving, as input, priorities for said location perturbation, said space perturbation, and rules and trade offs between said priorities;
calculating objective weights for said location perturbation, said space perturbation, and said rules based on said priorities and said trade offs using said computerized device;
modifying said objective weights to bias objectives of ones of said location perturbation, said space perturbation, and said rules having a same priority using said computerized device;
calculating an initial total objective cost based on said priorities, said trade offs and said minimum perturbation weights and said objective weights using said computerized device;
scaling said initial minimum perturbation weights and said objective weights to maintain subsequent total objective costs in a numeric range, while maintaining relativity, to produce scaled perturbation weights and scaled objective weights, wherein said scaling comprises assigning a weight of a higher priority to be a multiple of a weight of a lower priority using said computerized device;
solving said layout optimization using said scaled perturbation weights and said scaled objective weights to produce a layout optimization using said computerized device; and
outputting said layout optimization.