Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction.
Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction.
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低估辅助全局局部协同差异进化及其在蛋白质结构预测中的应用
DOI：
10.1109/tevc.2019.2938531
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发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Zhang GJ
中科院分区：
文献类型：
--
作者：
Zhou XG;Peng CX;Liu J;Zhang Y;Zhang GJ
在差异进化中，各种突变策略显示出明显的优势。进化过程中多种策略的配合可能是有效的。本文提出了一种低估辅助的全局和局部合作DE，同时提高效率和效果。在该算法中，两个阶段，即全局探索和局部开发，在每一代执行。在全局搜索阶段，采用多个具有较强搜索能力的策略为每个目标个体生成一组试探向量。之后，自适应低估模型与自适应斜率控制参数，提出了评估这些试验向量，其中最好的被选为候选人。在局部阶段，由比目标个体更好的个体指导的基于更好的策略被设计。对于在全局阶段接受的每个个体，通过使用这些策略生成多个试验向量，并通过低估值进行过滤。全球阶段与地方阶段的合作包括两个方面。首先，他们都致力于为下一代培养更好的人才。其次，全球阶段的目的是快速定位有前途的地区，而本地阶段作为一个本地搜索，以提高收敛。此外，设计了一种简单的机制，在搜索过程中自适应地确定DE的参数。最后，将该方法应用于蛋白质三维结构的预测。在经典基准函数、CEC测试集和蛋白质结构预测问题上的实验研究表明，该方法优于其他方法，具有上级性能。
Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This paper presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with a self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3D structure. Experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors.
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影响因子：
12.3
作者：
Elsayed, Saber M.;Sarker, Ruhul A.;Essam, Daryl L.
通讯作者：
Essam, Daryl L.
影响因子：
1.8
作者：
Batten, LM;Beliakov, G
通讯作者：
Beliakov, G
DOI：
10.1016/j.engappai.2013.09.013
发表时间：
2014-01-01
影响因子：
8
作者：
Elsayed, Saber M.;Sarker, Ruhul A.;Essam, Daryl L.
通讯作者：
Essam, Daryl L.
影响因子：
5.8
作者：
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通讯作者：
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影响因子：
11.8
作者：
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通讯作者：
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