Genetic Algorithm Optimization with Constraint Offset
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optimization calculation methods, such as genetic algorithms, face inefficiencies and lack reproducibility due to high dependence on initial values and repetitive trial-and-error processes, especially when dealing with constraint violations in design optimization problems like inductor core design, leading to excessive evaluation of individuals and reduced work efficiency.
Innovation Solution
The method generates individuals for each generation using a selected parent individual, evaluates them with a predetermined function, calculates constraint condition values based on previous generation values, and applies offsets to random numbers for mutation, ensuring a higher probability of satisfying constraint conditions, thus reducing the number of evaluations needed and improving reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of child individuals are generated and evaluated in advance to extract those satisfying constraint conditions, then the probability of finding feasible solutions increases, but the computational cost and number of evaluations increase excessively
Solution Approach 1:
The patent applies preliminary action by calculating constraint condition values in advance for the current generation based on previous generation data before performing evaluations. This pre-calculation allows the system to identify individuals likely to satisfy constraints ahead of time, reducing the need to evaluate excessive numbers of child individuals and improving evaluation efficiency while maintaining constraint satisfaction rates.
2Adaptability or versatility
If the optimization process relies on repetitive trial and error with manual adjustment, then flexibility in handling constraint violations is improved, but work efficiency and reproducibility deteriorate
Solution Approach 1:
The patent implements feedback by automatically calculating constraint condition values for the current generation based on previous generation results and using this information to guide subsequent optimization steps. This automated feedback loop replaces manual trial-and-error adjustments, maintaining the ability to handle constraint violations flexibly while significantly improving work efficiency and reproducibility through systematic automated decision-making.
3Measurement precision
If constraint condition values are calculated based on previous generation data and current generation provisional values, then the accuracy of constraint evaluation is improved, but the calculation complexity increases
Solution Approach 1:
The patent reduces calculation complexity through preliminary action by pre-calculating constraint condition values for the current generation before detailed evaluations are performed. This approach maintains high constraint evaluation accuracy by using both previous generation data and current generation provisional values, while avoiding the need for complex real-time calculations during the evaluation phase.
Data Source
AI summary
An optimization calculation method includes: generating, by a computer, current generation individuals with a selected previous generation individual as a parent individual; evaluating each current generation individual by using a predetermined evaluation function; calculating a current generation constraint condition value based on a previous generation constraint condition value and a constraint condition provisional value which is achieved by more than half of the current generation individuals; determining whether a result of the evaluation for each current generation individual satisfies the current generation constraint condition value; determining a predetermined offset based on an attribute of each individual, which is generated by a mutation generating process, among individuals having the evaluation results satisfying the current generation constraint condition value; and adding the predetermined offset to a random number used to generate each next generation individual by the mutation generating process.