Multi-Objective Design Selection via Gain and Loss Thresholds
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Solution Overview
Problem
In multi-objective optimization, decision-makers face challenges in selecting a single solution from the Pareto Frontier, as all solutions within the optimal set are equally optimal, making the selection process subjective and dependent on manual filtering or automated elimination based on density.
Innovation Solution
A computerized method and system that utilize a processor to select a group of multi-objective designs by matching objective values with gain and loss thresholds, providing a visualization of Pareto Frontier solutions and highlighting tradeoff patterns to aid in the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-objective optimization is used to generate the Pareto Frontier, then the comprehensiveness of solution coverage is improved, but the difficulty of selecting a single optimal solution increases
Solution Approach 1:
The patent transforms the subjective selection process into an objective one by changing the parameters used for evaluation. Instead of relying on decision-maker preferences, the system uses automated density-based metrics (k-distance, reachability density, crowding distance) to quantify and compare solutions, thereby resolving the contradiction between comprehensive coverage and selection difficulty
Solution Approach 2:
The patent implements feedback mechanisms where the selected solution is evaluated against multiple density metrics, and the selection process can be iteratively refined. The system provides feedback on solution quality through density measurements, allowing for automated re-selection if criteria are not met, thus improving both solution coverage and selection ease
2Measurement precision
If automated density-based selection is used, then the objectivity of solution selection is improved, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by computing density metrics only for the Pareto Frontier solutions rather than all possible solutions. The system calculates k-distance and reachability density selectively for boundary and interior points of the Pareto set, reducing computational overhead while maintaining selection objectivity
Solution Approach 2:
The patent segments the Pareto Frontier into boundary points and interior points, applying different density calculation methods to each segment. Boundary points use k-distance metrics while interior points use reachability density, dividing the computational task into manageable segments that reduce overall complexity
Data Source
AI summary
A method of selecting a group from a plurality of multi objective designs which comply with a plurality of objectives. The method comprises providing a plurality of multi objective designs, each the multi objective design having a plurality of multi objective design objective values which comply with at least one constraint of a Pareto Frontier of an objective space of a plurality of objectives, selecting a group from the plurality of multi objective designs, each member of the group is selected according to a match between at least one objective of respective the plurality of objectives and at least one of a respective gain threshold and a respective loss threshold, and outputting the group.