Information Processing Device Pullback Constraint Optimization
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Solution Overview
Problem
In multi-objective optimization problems, such as product design, the application of constraint conditions can lead to increased calculation times and instability due to the risk of not satisfying all constraint conditions, especially when the number of constraint conditions increases.
Innovation Solution
An information processing method that includes a crossover step, mutation step, and sorting step, along with pullback and individual constraint determination steps, where individuals deviating from constraint conditions are replaced with clones of parent individuals, ensuring stability and reducing calculation time by preventing prolonged iteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple constraint conditions are applied to ensure feasibility, then solution reliability is improved, but calculation time increases and stability deteriorates
Solution Approach 1:
The patent applies preliminary action by performing a pullback process on explanatory variables before the main optimization calculation. By pre-adjusting variables to satisfy constraint conditions, the system avoids time-consuming iterations during the optimization process, thus reducing overall calculation time while maintaining solution reliability.
Solution Approach 2:
The patent extracts and handles constraint satisfaction as a separate preliminary process (pullback process) before the main evolutionary optimization. This separation allows constraint checking and adjustment to be performed independently, reducing the computational burden during the main optimization loops and improving efficiency.
2Reliability
If the number of constraint conditions increases to ensure feasibility, then solution reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments constraint conditions into two categories: pullback constraints (applied to explanatory variables) and individual constraints (applied to individuals). This segmentation allows the system to manage multiple constraints systematically through dedicated processing steps, reducing the complexity of handling numerous constraints simultaneously.
Solution Approach 2:
The patent introduces an intermediary pullback process that mediates between raw explanatory variables and the main optimization process. This intermediary step handles constraint satisfaction uniformly, simplifying the management of multiple constraint conditions by providing a standardized interface for constraint handling.
3Reliability
If iterative calculation is performed to satisfy all constraint conditions, then solution reliability is improved, but calculation amount increases
Solution Approach 1:
The patent performs constraint satisfaction actions preliminarily through the pullback process before iterative optimization begins. By pre-adjusting explanatory variables to meet pullback constraints, the system reduces the number of iterations needed during optimization, thereby decreasing the total calculation amount while ensuring constraint compliance.
Solution Approach 2:
The patent applies partial action by selectively applying different constraint types at different stages: pullback constraints are applied preliminarily to all individuals, while individual constraints are applied selectively during optimization. This partial application strategy reduces unnecessary calculations while maintaining overall constraint satisfaction.
Data Source
AI summary
An information processing device determines presence or absence of an explanatory condition deviating from the pullback constraint, which is a constraint condition across a plurality of explanatory variables for each individual that has been generated, resets a reset value satisfying the pullback constraint with respect to the plurality of explanatory variables deviating from the pullback constraint, determines whether or not each of reset values of the plurality of explanatory variables satisfies an individual constraint condition, which is a constraint condition related to each explanatory variable, and replaces a constraint deviation individual, which is an individual having a reset value of an explanatory variable deviating from the individual constraint condition, with a clone of an individual selected from the n individuals when the constraint deviation individual is present.