Elite knowledge transfer within lower-level searches for bilevel optimization

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-08-06 DOI:10.1016/j.swevo.2025.102097

Yutao Lai, Hai-Lin Liu, Yukai Xu, Lei Chen

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引用次数: 0

Abstract

Bilevel optimization problems pose significant challenges for evolutionary algorithms (EAs) due to their nested structure. This paper introduces an efficient evolutionary bilevel algorithm that leverages elite knowledge transfer to tackle these challenges. Firstly, this paper employs a biobjective source selection strategy to balance convergence quality with relevance to the target lower-level problem. Building on this, a multi-source elite knowledge transfer mechanism constructs an elite Gaussian distribution model from source lower-level solutions, facilitating efficient parameterized knowledge transfer to accelerate the optimization of the target lower-level problem. Additionally, an adaptive strategy for reducing the lower-level population size further enhances algorithmic efficiency. Evaluated on benchmark test suites and real-world problems, the proposed algorithm demonstrates superior efficiency and accuracy compared to state-of-the-art bilevel optimization algorithms, underscoring the effectiveness of the elite knowledge transfer and adaptive reduction strategies. The source code for EKTBO has been publicly released at the following link: https://github.com/tg980515/EKTBO

查看原文本刊更多论文

精英知识在低层次搜索中转移，以实现双层优化

双层优化问题由于其嵌套结构，对进化算法提出了重大挑战。本文介绍了一种有效的进化双层算法，利用精英知识转移来解决这些挑战。首先，本文采用双目标源选择策略来平衡收敛质量和与目标下层问题的相关性。在此基础上，多源精英知识转移机制从源底层解构建精英高斯分布模型，实现高效的参数化知识转移，加速目标底层问题的优化。此外，采用自适应策略减少下层种群的大小，进一步提高了算法的效率。通过对基准测试套件和实际问题的评估，与最先进的双层优化算法相比，该算法显示出更高的效率和准确性，强调了精英知识转移和自适应约简策略的有效性。EKTBO的源代码已在以下链接公开发布：https://github.com/tg980515/EKTBO

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.