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Rotated neighbor learning-based auto-configured evolutionary algorithm

基于旋转邻域学习的自配置进化算法

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Abstract

More and more evolutionary operators have been integrated and manually configured together to solve wider range of problems. Considering the very limited progress made on the automatic configuration of evolutionary algorithms (EAs), a rotated neighbor learning-based auto-configured evolutionary algorithm (RNLACEA) is presented. In this framework, multiple EAs are combined as candidates and automatically screened for different scenarios with a rotated neighbor structure. According to a ranking record and a group of constraints, the algorithms can be better scheduled to improve the searching efficiency and accelerate the searching pace. Experimental studies based on 14 classical EAs and 22 typical benchmark problems demonstrate that RNLACEA outperforms other six representative auto-adaptive EAs and has high scalability and robustness in solving different kinds of numerical optimization problems.

摘要

创新点

本文提出了一种旋转邻域学习的自配置进化算法。 通过多种进化算子的集合形成底层备选池, 我们在进化个体基础上建立了一种新型旋转邻域结构, 使得个体能在O(nlogn)时间内在种群内传播其自身进化信息和所使用的算子记录。 同时, 通过与邻域个体的信息比较和算子排列记录, 个体能自主并快速地自动选取当前所需的进化操作, 最终提升进化算法整体的搜索能力和扩展性。 大量基于数值优化标准函数的实验充分证明了本文所设计的自配置进化算法的有效性、 鲁棒性及其扩展性。

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Yuanjun Laili, Lin Zhang, Fei Tao & Pingchuan Ma

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  1. Yuanjun Laili
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  2. Lin Zhang
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  3. Fei Tao
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  4. Pingchuan Ma
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Correspondence to Lin Zhang.

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Laili, Y., Zhang, L., Tao, F. et al. Rotated neighbor learning-based auto-configured evolutionary algorithm. Sci. China Inf. Sci. 59, 052101 (2016). https://doi.org/10.1007/s11432-015-5372-0

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  • DOI: https://doi.org/10.1007/s11432-015-5372-0

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