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

2025-06-03

AI Summary

In bilevel optimization, evaluating upper-level solutions requires repeated resolution of the lower-level problem, leading to substantial computational redundancy. This paper proposes an adaptive resource allocation framework that models the upper–lower-level solution relationship online and dynamically identifies and prioritizes high-potential lower-level tasks. Its core contribution is a reference-based ranking strategy driven by a contrastive ranking network, enabling population-quality-aware adaptive resampling—overcoming limitations of conventional static or heuristic allocation schemes. Evaluated across five state-of-the-art bilevel evolutionary algorithms, the framework reduces function evaluations by 37.2% on average while preserving or improving solution accuracy. It exhibits strong generalizability and plug-and-play compatibility.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Reduces redundant lower-level task evaluations in bilevel EAs
• Adaptively allocates resources to promising lower-level tasks
• Improves computational efficiency without sacrificing solution accuracy

Innovation

• Contrastive ranking network for task prioritization
• Adaptive resampling based on population quality
• Selective focus on promising lower-level tasks