Hierarchical Multi-Objective Optimization Algorithm
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Solution Overview
Problem
Existing optimization algorithms for multiple objectives often require arbitrary weight assignments or user selection, leading to suboptimal solutions and inefficient use of computational resources, particularly in material design and engineering where simultaneous optimization of multiple properties is necessary.
Innovation Solution
A hierarchical multi-objective optimization system that evaluates objectives sequentially based on defined target values and penalty values, allowing for early termination and efficient use of computational resources, without the need for arbitrary weight assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If arbitrary weight assignments or Pareto optimization are used to solve multi-objective problems, then the optimization process can be performed, but the solutions become suboptimal and computational resources are wasted
Solution Approach 1:
The patent segments the multi-objective optimization into a hierarchical structure where objectives are divided into levels (primary, secondary, tertiary). This segmentation allows the system to evaluate objectives in order of importance, terminating evaluation when primary objectives are satisfied, thereby avoiding wasted computational resources on lower-priority objectives that cannot be achieved.
Solution Approach 2:
The patent implements preliminary evaluation of objectives in hierarchical order before full optimization. By assessing primary objectives first and only proceeding to secondary objectives if primary ones are met, the system performs preliminary filtering that prevents unnecessary computational exploration of infeasible solution spaces.
2Measurement precision
If all objective functions are evaluated for every candidate input, then complete optimization information is obtained, but computational resources are consumed inefficiently
Solution Approach 1:
The patent segments objective function evaluation into hierarchical levels. Primary objectives are evaluated first, and only if they are satisfied does the system proceed to evaluate secondary objectives. This segmentation enables the system to obtain sufficient optimization information without the computational burden of evaluating all objectives for every candidate input.
Solution Approach 2:
The patent applies partial action by evaluating only the necessary subset of objectives based on hierarchical priority. Rather than excessively evaluating all objectives regardless of outcome, the system stops evaluation once primary objectives are assessed, accepting that this partial evaluation is sufficient for determining feasibility and guiding further optimization.
3Reliability
If penalty values are assigned to candidate inputs that fail objective conditions, then optimization guidance is provided, but computational time is increased
Solution Approach 1:
The patent implements feedback through penalty values assigned to candidate inputs that fail to satisfy objective conditions. These penalties provide quantitative guidance on solution quality and drive the optimization process toward feasible regions. The feedback mechanism is integrated with hierarchical evaluation, allowing efficient convergence without excessive computational time.
Data Source
AI summary
A method for optimizing objective functions can include selecting an objective function based at least on a hierarchy, applying parameters to the objective function to generate an output, responsive to the output not satisfying a tolerance condition, assigning a penalty to the set of parameters and evaluating a convergence condition using the set of parameters and the penalty, responsive to the output satisfying the tolerance condition, evaluating an additional objective function using the parameters in an order corresponding to the hierarchy or evaluating the convergence condition responsive to the selected objective function being a final objective function, modifying the set of parameters using a genetic algorithm responsive to the set of parameters not satisfying the convergence condition, and outputting the set of parameters responsive to the set of parameters satisfying the convergence condition.