Parallel Evolutionary Search Space Segmentation for High-Dimensional Optimization
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Solution Overview
Problem
Existing methods for high-dimensional search space optimization are inefficient and cannot effectively utilize distributed computing resources, leading to increased computing time and reduced solution accuracy.
Innovation Solution
A parallel evolutionary solution method for search space segmentation is introduced, which involves randomly generating initial sample schemes, calculating fitness values, and using principal component analysis to segment the search space into subspaces. This allows for parallel search and local optimization using differential evolution algorithms and local search algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional search space optimization is performed using existing methods, then solution accuracy can be maintained, but computing time increases significantly and distributed computing resources cannot be effectively utilized
Solution Approach 1:
The search space is segmented into multiple subspaces based on fitness terrain analysis. Each subspace is then optimized independently using parallel computational processes, allowing distributed resources to be effectively utilized while maintaining solution accuracy through coordinated exploration of all subspaces
2Productivity
If distributed computing resources are utilized for high-dimensional optimization, then computing efficiency improves, but the ability to effectively segment and guide the search space deteriorates
Solution Approach 1:
Fitness terrain analysis is performed in advance to identify key dimensions and optimal segmentation points before the main optimization process begins. This preliminary analysis provides a structured framework that guides subsequent parallel search operations, reducing the complexity of coordinating distributed computing resources
3Area of stationary object
If grid-based segmentation is used for low-dimensional problems, then the search space can be effectively divided, but this approach becomes powerless for high-dimensional problems
Solution Approach 1:
The segmentation approach transitions from fixed grid-based division in low dimensions to adaptive fitness-terrain-guided segmentation in high dimensions. The method dynamically adjusts segmentation parameters based on the analyzed fitness landscape, allowing effective search space division across different dimensionalities by changing the segmentation strategy rather than relying on a single fixed approach
Data Source
AI summary
The invention discloses a parallel evolutionary solution method for search space segmentation, comprising: S1, randomly generating solution schemes via a search space sampling and segmentation module, calculating fitness values by combining initial sample schemes with an optimization objective function, statistically screening sample schemes, determining the dimension direction K of the segmented search space, and dividing the search space into subspaces; S2, executing a global search algorithm in the segmented subspaces to obtain a global initial solution scheme; S3, using the global initial solution scheme as a starting point, obtaining a precise solution scheme via a local search algorithm. This method employs evolutionary sampling of the search space, deriving fitness terrain analysis results, and guiding the division direction and step size of the search space. Multiple search subspaces are segmented to enable parallel search for subsequent evolutionary calculations.