Evolutionary Search Direction Control for Uniform Pareto Solutions
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Solution Overview
Problem
In multi-objective optimization, the solution search direction is fixed, leading to a risk of obtaining highly uniform Pareto solutions.
Innovation Solution
A calculation program dynamically changes the solution search direction based on the distribution of Pareto solutions by setting control points using the hypervolume concept to optimize the evaluation function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a fixed solution search direction is used in multi-objective optimization, then the optimization process is simple and stable, but the uniformity of Pareto solutions deteriorates
Solution Approach 1:
The patent applies the dynamics principle by making the solution search direction changeable rather than fixed. The optimization direction is dynamically adjusted based on the distribution state of current Pareto solutions, allowing the search to adapt to different regions of the solution space and achieve more uniform distribution of Pareto solutions while maintaining process stability through systematic control mechanisms
Solution Approach 2:
The patent implements feedback by using the distribution information of current Pareto solutions to guide the search direction for the next generation. The system continuously monitors the distribution state and feeds this information back to adjust the optimization direction, creating a closed-loop control mechanism that improves solution uniformity while maintaining stability
2Manufacturing precision
If the search direction is dynamically changed based on Pareto solution distribution, then the uniformity of Pareto solutions is improved, but the calculation complexity increases
Solution Approach 1:
The patent applies parameter changes by modifying the search direction parameters based on the distribution characteristics of Pareto solutions. Instead of fundamentally changing the optimization algorithm structure, it adjusts directional parameters dynamically, which improves solution uniformity while keeping the increase in calculation complexity manageable through parameter-based control rather than structural complexity
3Adaptability or versatility
If evolutionary computation is used to search for solutions with multiple objective functions, then the ability to handle multi-objective optimization is improved, but the risk of obtaining non-uniform Pareto solutions increases
Solution Approach 1:
The patent combines evolutionary computation with dynamic direction adjustment, maintaining the adaptability and versatility of evolutionary algorithms for handling multiple objective functions while introducing dynamic control of search directions to improve the uniformity of obtained Pareto solutions
Solution Approach 2:
The patent integrates feedback mechanisms into evolutionary computation by using distribution information of current Pareto solutions to guide the evolution direction, allowing the system to maintain multi-objective optimization capability while achieving more uniform solution distribution through continuous feedback and adjustment
Data Source
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AI summary
A calculation program causes a computer to execute, when repeatedly searching for a solution using evolutionary computation based on an evaluation function that evaluates multiple objective functions, control a search direction for the solution according to a distribution of Pareto solutions obtained, and searching for a next generation of solutions.