Information Processing Device for Multi-Objective Optimization Constraint Handling
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Solution Overview
Problem
In multi-objective optimization using evolutionary algorithms, the generation of child individuals that satisfy constraint conditions across multiple explanatory variables can be time-consuming, leading to increased calculation times and potential convergence issues or local solutions due to deviations from constraint conditions.
Innovation Solution
An information processing method that includes a crossover step, mutation step, sorting step, pullback constraint determination, and pullback step to generate and evaluate new individuals, ensuring that the sum of explanatory variables falls within a predetermined range while maintaining their ratio, thereby reducing calculation time and maintaining high fitness degrees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constraint conditions are checked after generating child individuals, then constraint satisfaction can be ensured, but calculation time increases significantly and convergence may be delayed
Solution Approach 1:
The patent applies preliminary action by checking constraint conditions during the generation process of child individuals rather than after complete generation. The constraint determination unit identifies violations early in the crossover or mutation processes, allowing immediate correction through pullback operations. This prevents the need to generate complete invalid individuals and then discard them, significantly reducing calculation time while ensuring constraint satisfaction.
2Manufacturing precision
If constraint violations cause elimination and regeneration of individuals, then constraint accuracy is maintained, but the calculation may not converge or may reach local solutions
Solution Approach 1:
The patent implements feedback mechanisms where the constraint determination unit continuously monitors constraint satisfaction during individual generation. When violations are detected, the pullback unit provides corrective feedback by adjusting explanatory variables back into the feasible region. This continuous feedback loop guides the evolutionary process toward valid solutions without abrupt eliminations, maintaining both constraint accuracy and convergence reliability by preserving the evolutionary direction while correcting violations.
3Manufacturing precision
If knowledge of optimal direction from algorithm cannot be utilized due to constraint checking, then local solutions may be found, but enforcing constraints strictly preserves solution accuracy
Solution Approach 1:
The patent applies partial action by selectively applying constraint checking and pullback operations only when violations are detected during the evolutionary process, rather than enforcing constraints at every generation unconditionally. This allows the algorithm to freely explore and utilize optimal direction knowledge when constraints are satisfied, while intervening only when necessary to correct violations. This balanced approach maintains solution accuracy while preserving optimization efficiency by avoiding excessive constraint enforcement.
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 based on four arithmetic operations across a plurality of explanatory variables for each individual that has been generated and resets a reset value based on a plurality of explanatory variables with respect to the plurality of explanatory variables deviating from the pullback constraint.