Offline evolutionary optimization with problem-driven model pool design and weighted model selection indicator

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huixiang Zhen , Bing Xue , Wenyin Gong , Mengjie Zhang , Ling Wang
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引用次数: 0

Abstract

Offline data-driven evolutionary algorithms aim to provide a promising solution based on the collected historical data, without online real fitness evaluations. However, the suitability of surrogate models varies significantly across different problem types, and current research often overlooks the relationship between problem characteristics and model performance. To address this gap, we propose a novel offline data-driven evolutionary algorithm, termed MSEA, which integrates a problem-driven model pool design and a weighted indicator-based model selection mechanism. The model pool is carefully designed, incorporating four distinct surrogate models tailored for various optimization landscapes to align with diverse problem characteristics. A weighted selection indicator, derived from both model evaluation and solution quality assessment, is employed to dynamically select the most suitable model for the optimization problem. Extensive experimental results demonstrate that MSEA effectively identifies and utilizes the optimal model from the pool for specific offline optimization tasks. Compared to five state-of-the-art offline data-driven methods, MSEA achieved optimal results for 26 out of 32 functions across dimensions ranging from 10 to 100 and also exhibited faster running times. Furthermore, in high-dimensional spaces, MSEA achieved the best optimization results in dimensions ranging from 200 to 500. Our code is available at https://github.com/zhenhuixiang/MSEA.
基于问题驱动模型池设计和加权模型选择指标的离线进化优化
离线数据驱动的进化算法旨在提供基于收集的历史数据的有前途的解决方案,而不需要在线的真实适应度评估。然而,代理模型的适用性在不同的问题类型之间存在显著差异,目前的研究往往忽略了问题特征与模型性能之间的关系。为了解决这一差距,我们提出了一种新的离线数据驱动进化算法,称为MSEA,它集成了问题驱动的模型池设计和基于加权指标的模型选择机制。模型池经过精心设计,结合了四个不同的代理模型,为各种优化场景量身定制,以适应不同的问题特征。采用模型评价和解质量评价相结合的加权选择指标,动态选择最适合优化问题的模型。大量的实验结果表明,MSEA可以有效地识别和利用池中的最优模型来完成特定的离线优化任务。与五种最先进的离线数据驱动方法相比,MSEA在32个函数中的26个(维度从10到100)上实现了最佳结果,并且显示出更快的运行时间。此外,在高维空间中,MSEA在200 ~ 500维范围内的优化效果最好。我们的代码可在https://github.com/zhenhuixiang/MSEA上获得。
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来源期刊
Swarm and Evolutionary Computation
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.
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