Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy
Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy
复制标题
基于准物理策略的粒子群优化 Lennard-Jones 簇的优化
DOI：
10.1016/j.swevo.2020.100710
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发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Zhu Yuanhao
中科院分区：
文献类型：
--
作者：
Mai Guizhen;Hong Yinghan;Fu Shen;Lin Yingqing;Hao Zhifeng;Huang Han;Zhu Yuanhao
Lennard-Jones(LJ)簇优化的目标是寻找簇的势函数的最小值，从而确定簇的稳定构型。它本质上是一个完全不可分的多峰全局优化问题，用传统的粒子群算法求解往往会导致局部收敛，这意味着算法的解精度不高。因此，在本研究中，我们使用粒子群优化(PSO)方法和物理方法开发了一种LJ算法来提高解的精度。在这种准物理策略(QPS)中，使用粒子群算法来模拟真实的原子结构，并结合原子间作用力来构建收敛模型，从而使算法在全局和局部空间都具有良好的性能。选取不同LJ集群系统的势能函数作为测试函数，将改进的粒子群算法(QPS-PSO)与竞争群优化算法、协作协进化粒子群算法、差分群协同进化粒子群算法、变长特征选择粒子群算法、异构型综合学习粒子群算法、集成粒子群算法和协同进化差分优化粒子群算法进行了分析比较。结果表明，基于QPS的粒子群优化算法在高维空间中具有明显的解精度优势。
The goal of Lennard-Jones (LJ) clusters optimization is to find the minimum value of the potential function of a cluster and thereby determine the stable configuration of the cluster. It is essentially a completely inseparable multimodal global optimization problem, and using the traditional particle swarm algorithm to solve it often results in local convergence, which means that the solution accuracy of the algorithm is not high. Thus, in this study, we develop a LJ algorithm using a particle swarm optimization (PSO) method and a physical approach to improve the solution accuracy. In this quasi-physical strategy (QPS), the particle swarm algorithm is used to simulate the real atomic structure and incorporates the interatomic force to construct a convergence model so that the algorithm performs well in both global and local space. The potential energy functions of a variety of LJ cluster systems are selected as test functions, and the improved PSO algorithm (QPS-PSO) is analyzed and compared with a competitive swarm optimizer, cooperative coevolution PSO, and differential-group cooperative coevolution, variable-length PSO for feature selection, heterogeneous comprehensive learning PSO, ensemble PSO and cooperative coevolution with differential optimization. The results show that the PSO algorithm for LJ clusters using the proposed QPS has noticeably superior solution accuracy, especially in high-dimensional spaces.
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影响因子：
2.8
作者：
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通讯作者：
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DOI：
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发表时间：
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DOI：
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期刊：
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影响因子：
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DOI：
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发表时间：
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期刊：
Numerical Algebra, Control and Optimization
影响因子：
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