Adaptive Search Direction for Uniform Pareto Optimization
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Solution Overview
Problem
Multi-objective optimization methods often result in non-uniform distribution of Pareto solutions due to fixed search directions, limiting the breadth and convergence of optimal solutions.
Innovation Solution
A method that dynamically adjusts the solution search direction based on the distribution of Pareto solutions by setting control points within sparse areas, using the hypervolume metric to guide the search towards more uniform solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed search direction is used in multi-objective optimization, then the optimization process is simple to implement, but the distribution of Pareto solutions becomes non-uniform
Solution Approach 1:
The patent applies dynamics by making the search direction adaptive rather than fixed. The algorithm dynamically adjusts the search direction based on the distribution of obtained Pareto solutions, allowing the optimization process to respond to the actual solution landscape and achieve more uniform distribution while maintaining computational feasibility
Solution Approach 2:
The patent implements feedback by using the distribution information of obtained Pareto solutions to guide subsequent search directions. The algorithm continuously monitors the solution distribution and adjusts the search strategy accordingly, creating a closed-loop system that improves uniformity through iterative refinement
2Manufacturing precision
If the search direction is dynamically adjusted based on Pareto solution distribution, then the uniformity of Pareto solutions is improved, but the computational complexity increases
Solution Approach 1:
The patent changes parameters by adjusting the search direction based on the distribution characteristics of Pareto solutions. By monitoring solution density and modifying search parameters dynamically, the algorithm achieves improved uniformity without requiring fundamentally more complex computational structures
Solution Approach 2:
The patent performs preliminary actions by establishing a framework that proactively guides the search toward under-explored regions. By anticipating areas needing exploration and adjusting directions in advance, the algorithm reduces the need for extensive random searching, thereby managing computational complexity
3Manufacturing precision
If the search direction is dynamically adjusted based on Pareto solution distribution, then the convergence of solutions is improved, but the calculation time increases
Solution Approach 1:
The patent uses feedback from the distribution of obtained solutions to guide subsequent searches more effectively toward convergence. By continuously adjusting the search direction based on observed solution patterns, the algorithm accelerates convergence while managing computational overhead through intelligent direction selection
Solution Approach 2:
The dynamic adjustment of search direction allows the algorithm to adapt to the evolving solution landscape, concentrating computational effort in regions that will most improve convergence. This dynamic reallocation of search effort improves convergence speed relative to static approaches
Data Source
AI summary
A non-transitory computer-readable recording medium that stores a program causing a computer to execute a process is provided. The process includes when repeatedly searching for a solution using evolutionary computation based on an evaluation function that evaluates multiple objective functions, controlling a search direction for the solution according to a distribution of Pareto solutions obtained, and searching for a next generation of solutions.