CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms

• URL: http://arxiv.org/abs/2506.06362v1
• Date: Tue, 03 Jun 2025 17:31:49 GMT
• Title: CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
• Authors: Dejun Xu, Jijia Chen, Gary G. Yen, Min Jiang,
• Abstract summary: Bilevel optimization poses a significant computational challenge due to its nested structure.<n>We propose a novel resource allocation framework for bilevel evolutionary algorithms.<n>Our framework significantly reduces computational cost while preserving -- or even enhancing -- solution accuracy.
• Score: 9.411648722302711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bilevel optimization poses a significant computational challenge due to its nested structure, where each upper-level candidate solution requires solving a corresponding lower-level problem. While evolutionary algorithms (EAs) are effective at navigating such complex landscapes, their high resource demands remain a key bottleneck -- particularly the redundant evaluation of numerous unpromising lower-level tasks. Despite recent advances in multitasking and transfer learning, resource waste persists. To address this issue, we propose a novel resource allocation framework for bilevel EAs that selectively identifies and focuses on promising lower-level tasks. Central to our approach is a contrastive ranking network that learns relational patterns between paired upper- and lower-level solutions online. This knowledge guides a reference-based ranking strategy that prioritizes tasks for optimization and adaptively controls resampling based on estimated population quality. Comprehensive experiments across five state-of-the-art bilevel algorithms show that our framework significantly reduces computational cost while preserving -- or even enhancing -- solution accuracy. This work offers a generalizable strategy to improve the efficiency of bilevel EAs, paving the way for more scalable bilevel optimization.

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