Decision Analysis System Using Topological Clustering for Pareto Front Approximation
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Solution Overview
Problem
Current decision analysis methods face challenges in balancing conflicting priorities and require significant computational resources, making them impractical for efficient problem-solving.
Innovation Solution
A system and method that utilize a processor to receive input data with conflicting parameters, generate solutions using a multi-objective evolutionary algorithm, approximate a Pareto front, and present solution archetypes through a topological clustering algorithm, displayed on a visual interface, reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-objective optimization methods are used to address conflicting priorities, then the quality of decision solutions is improved, but computational resources required increase significantly
Solution Approach 1:
The patent segments the continuous Pareto front into discrete clusters of solution archetypes. Instead of computing and presenting the entire continuous set of optimal solutions, the system identifies and groups representative solutions into distinct categories (e.g., aggressive, conservative, balanced strategies), significantly reducing computational burden while maintaining decision quality.
Solution Approach 2:
The patent creates simplified representations (copies) of the complex optimization problem by generating solution archetypes that capture the essential characteristics of multiple optimal solutions. These archetypes serve as representative copies that convey the trade-off structure without requiring full computation of the entire Pareto front.
2Adaptability or versatility
If multiple solutions representing different tradeoffs are generated, then the comprehensiveness of decision options is improved, but the complexity of the solution set increases
Solution Approach 1:
The patent merges multiple similar solutions into clustered groups representing distinct solution archetypes. By combining solutions with similar characteristics into single representative categories, the system maintains comprehensive coverage of decision options while reducing the apparent complexity from the user's perspective.
Solution Approach 2:
The patent applies partial action by selecting and presenting only the most representative solution archetypes rather than all possible solutions. This partial presentation approach provides sufficient decision comprehensiveness without overwhelming the user with excessive detail.
Data Source
AI summary
Systems and methods are provided for generating solutions to decision analysis problems. The systems include a processor configured to: receive input data relating to a decision analysis problem, the input data including various parameters, wherein one or more of the parameters are in conflict, generate a plurality of the solutions based on the input data, wherein each of the plurality of solutions are from distinct homotopy classes, approximate a Pareto front using a multi-objective evolutionary algorithm, the Pareto front representing a collection of the plurality of the solutions that are not inferior to others of the plurality of the solutions in view of an entirety of the parameters in the input data, generate a course of action (COA) menu presenting solution architypes based on the Pareto front using a topological clustering algorithm, and display the COA menu on a visual display device.