Dynamic Multi-Objective Optimization With User Preference Feedback
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Solution Overview
Problem
Multi-objective optimization problems require exploring large design spaces efficiently, but existing methods lack the ability to incorporate user preferences dynamically, leading to suboptimal solutions due to the complexity of tradeoffs between multiple objectives.
Innovation Solution
A method and apparatus that allow users to input preferences during the optimization process, adjusting weights of intermediate designs based on user feedback, thereby influencing the exploration of the design space to prioritize desired solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the optimization process explores the entire design space to find optimal solutions, then the solution quality improves, but the computational resource consumption increases
Solution Approach 1:
The patent implements a feedback mechanism where user preferences about intermediate designs are incorporated into the optimization process. The system receives feedback indicating which intermediate designs are preferred, adjusts the optimization strategy accordingly, and uses this feedback to guide future exploration of the design space, thereby reducing unnecessary computational resources while maintaining solution quality.
Solution Approach 2:
The optimization process is made dynamic by allowing real-time adjustment of the search strategy based on user feedback. The system transitions from a static, predetermined exploration path to a dynamic process that adapts its behavior based on intermediate results and user preferences, enabling more efficient use of computational resources.
2Measurement precision
If the optimization process runs for a longer duration to thoroughly explore the design space, then the solution quality improves, but the time consumption increases
Solution Approach 1:
The system incorporates feedback from user evaluations of intermediate designs to dynamically adjust the optimization timeline and exploration strategy. By receiving feedback about which designs are preferred, the system can prioritize promising areas of the design space and reduce time spent on less promising regions, thereby reducing overall time consumption while maintaining solution quality.
Solution Approach 2:
The system performs preliminary evaluation of intermediate designs and uses this information to pre-determine which areas of the design space warrant further exploration. This preliminary action allows the optimization process to allocate time more effectively, focusing computational effort on regions most likely to yield optimal solutions based on user preferences.
3Adaptability or versatility
If the optimization process uses more computational resources to evaluate more designs, then the coverage of the design space improves, but the cost increases
Solution Approach 1:
The patent uses feedback from user preferences to intelligently allocate computational resources across the design space. By knowing which intermediate designs are preferred, the system can concentrate computational effort on regions that align with user preferences, achieving adequate design space coverage with reduced overall computational resource consumption.
Solution Approach 2:
The optimization process applies local quality by concentrating computational resources on specific regions of the design space that show promise based on user feedback. Rather than uniformly distributing computational effort across the entire design space, the system intensifies exploration in locally promising areas, achieving effective coverage with fewer total resources.
Data Source
AI summary
A method and apparatus of a device that incorporates a user preference into a multi-objective optimization while the multi-objective optimization is running is described. In an exemplary embodiment, the device generates a first plurality of intermediate designs based on optimizing a plurality of variable values corresponding to the multiple dimensions of the multi-objective optimization. In addition, each of the first plurality of intermediate designs includes a corresponding weight. Furthermore, the device outputs the first plurality of intermediate designs. The device additionally receives a preference indicator for a selected one of the intermediate designs, where a user inputs the preference indicator while the multi-objective optimization is running. The device further adjusts the corresponding weight of the selected one of the first plurality of intermediate designs based on the preference indicator. In addition, the device generates a second plurality of intermediate designs using the adjusted corresponding weight of the selected one of the first plurality of intermediate designs.