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An adaptive dimension level adjustment framework for differential evolution

机译:差分演化的自适应尺寸调整框架

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摘要

Differential evolution (DE) has been recognized as one of the most popular evolutionary algorithms. There are numerous DE variants adopting multi-operators based cooperation strategy to improve their performance, but almost all of the adopted cooperation strategies are essentially implemented at the individual level or population level, and the implementation at the dimension level are scarce. In this paper, an adaptive dimension level adjustment (ADLA) framework is designed to relieve the premature convergence or stagnation problem faced by DE algorithm, which can be easily combined with diverse DE variants. When the current optimal individual cannot get improved for a given uninterrupted iterations, ADLA framework will be triggered to select some individuals at random according to specific rule and reinitialize portion of their dimensions from a dynamic search space that adjusted by a population level macroparameter and one individual level microparameter. Moreover, ADLA framework contains two reinitialization operators with different search characteristics, and the coordination between them is executed at the dimension level, which has potential advantages in balancing the global exploration ability and local exploitation ability. Extensive comparison experiments are carried out based on IEEE CEC 2014 test platform, two basic DE algorithms and six outstanding DE variants. The experimental results demonstrate that ADLA framework can memorably enhance the performance of every DE algorithm used for comparison. (C) 2020 Elsevier B.V. All rights reserved.
机译:差分进化(DE)被认为是最流行的进化算法之一。有许多DE VARIANTS采用基于多运营商的合作策略来提高其性能,但几乎所有采用的合作策略基本上在个人一级或人口层面实施,维度水平的实施是稀缺的。在本文中,设计自适应尺寸级调整(ADLA)框架旨在缓解DE算法面临的过早收敛或停滞问题,这可以容易地与多样化的DE VELIANTS组合。当当前最佳个体无法改善给定的不间断迭代时,将触发ADLA框架以根据具体规则随机选择一些单独的,并从由人口水平Macroparameter和一个人调整的动态搜索空间重新初始化其尺寸的部分水平微丙酰胺。此外,ADLA框架包含两个具有不同搜索特征的重新初始化运营商,并且它们之间的协调在维度级别执行,这在平衡全球勘探能力和本地利用能力方面具有潜在的优势。基于IEEE CEC 2014测试平台,两个基本的DE算法和六种优秀的DE VELIALS进行了广泛的比较实验。实验结果表明,ADLA框架可以难忘地增强了用于比较的每个DE算法的性能。 (c)2020 Elsevier B.v.保留所有权利。

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