An Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization

• URL: http://arxiv.org/abs/2410.24081v2
• Date: Wed, 06 Nov 2024 08:09:23 GMT
• Title: An Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization
• Authors: Dejun Xu, Kai Ye, Zimo Zheng, Tao Zhou, Gary G. Yen, Min Jiang,
• Abstract summary: Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. This paper proposes an efficient dynamic resource allocation framework for evolutionary bilevel optimization, named DRC-BLEA.
• Score: 14.51523276196879
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionary algorithms are commonly used to solve complex bilevel problems in practical scenarios, but they face significant resource consumption challenges due to the nested structure imposed by the implicit lower-level optimality condition. This challenge becomes even more pronounced as problem dimensions increase. Although recent methods have enhanced bilevel convergence through task-level knowledge sharing, further efficiency improvements are still hindered by redundant lower-level iterations that consume excessive resources while generating unpromising solutions. To overcome this challenge, this paper proposes an efficient dynamic resource allocation framework for evolutionary bilevel optimization, named DRC-BLEA. Compared to existing approaches, DRC-BLEA introduces a novel competitive quasi-parallel paradigm, in which multiple lower-level optimization tasks, derived from different upper-level individuals, compete for resources. A continuously updated selection probability is used to prioritize execution opportunities to promising tasks. Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence. Experimental results compared with chosen state-of-the-art algorithms demonstrate the effectiveness of the proposed method. Specifically, DRC-BLEA achieves competitive accuracy across diverse problem sets and real-world scenarios, while significantly reducing the number of function evaluations and overall running time.

Related papers

• CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms [9.411648722302711]
  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.
  arXiv  Detail & Related papers  (2025-06-03T17:31:49Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization [12.899626317088885]
  This paper introduces LCC, a pioneering learning-based cooperative coevolution framework. LCC dynamically schedules decomposition strategies during optimization processes. It offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption.
  arXiv  Detail & Related papers  (2025-04-24T14:09:22Z)
• SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization [35.92829763686735]
  This paper studies decentralized bilevel optimization, in which multiple agents collaborate to solve problems involving nested optimization structures with neighborhood communications. We propose SPARKLE, a unified Single-loop Primal-dual AlgoRithm frameworK for decentraLized bilEvel optimization. We present a unified convergence analysis for SPARKLE, applicable to all its variants, with state-of-the-art convergence rates compared to existing decentralized bilevel algorithms.
  arXiv  Detail & Related papers  (2024-11-21T14:23:06Z)
• Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling [96.47086913559289]
  gradient-based algorithms are widely used in bilevel optimization. We introduce a without-replacement sampling based algorithm which achieves a faster convergence rate. We validate our algorithms over both synthetic and real-world applications.
  arXiv  Detail & Related papers  (2024-11-07T17:05:31Z)
• A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints [66.61399765513383]
  We develop a BLOCC algorithm to tackle BiLevel Optimization problems with Coupled Constraints. We demonstrate its effectiveness on two well-known real-world applications.
  arXiv  Detail & Related papers  (2024-06-14T15:59:36Z)
• Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning [5.325297567945828]
  We propose a new method for two-time-scale optimization that achieves significantly faster convergence than the prior arts. We characterize the proposed algorithm under various conditions and show how it specializes on online sample-based methods.
  arXiv  Detail & Related papers  (2024-05-15T19:03:08Z)
• Contextual Stochastic Bilevel Optimization [50.36775806399861]
  We introduce contextual bilevel optimization (CSBO) -- a bilevel optimization framework with the lower-level problem minimizing an expectation on some contextual information and the upper-level variable. It is important for applications such as meta-learning, personalized learning, end-to-end learning, and Wasserstein distributionally robustly optimization with side information (WDRO-SI)
  arXiv  Detail & Related papers  (2023-10-27T23:24:37Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate [26.676582181833584]
  Decentralized multi-level optimization is challenging because of the multilevel structure and decentralized communication. We develop two novel decentralized optimization algorithms to optimize the multi-level compositional problem.
  arXiv  Detail & Related papers  (2023-06-06T00:23:28Z)
• Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems [118.00379425831566]
  We propose a communication-efficient algorithm, named FedBiOAcc. We prove that FedBiOAcc-Local converges at the same rate for this type of problems. Empirical results show superior performance of our algorithms.
  arXiv  Detail & Related papers  (2023-02-13T21:28:53Z)
• ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set [48.57120672468062]
  This paper introduces an efficient algorithm for the Maximum Independent Set (MIS) problem, incorporating two innovative techniques. The proposed algorithm outperforms state-of-the-art algorithms in terms of solution quality, computational efficiency, and stability.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning [0.6445605125467573]
  Evolutionary strategies have been shown to achieve competing levels of performance for complex optimization problems in reinforcement learning. Convergence guarantees for evolutionary strategies to optimize constrained problems are however lacking in the literature.
  arXiv  Detail & Related papers  (2022-02-21T17:04:51Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.