用蝠鲼觅食优化算法提升晶体能量预测精度
Evolutionary Extreme Learning Machine of ab-initio Energy Landscapes for Crystal Structure Prediction using Manta Ray Optimization with Levy Flight
- 融合莱维飞行的蝠鲼优化算法训练极限学习机
- 在二元体系中准确预测未弛豫与弛豫态形成能
- 适合材料结构预测与高通量计算研究者
蝠鲼觅食优化算法（MRFO）在众多工程问题中表现出强大的求解能力。本文提出一种结合莱维飞行（Levy Flight）的MRFO改进方法，用于训练极限学习机（ELM），其基础模型为单层前馈网络（SLFN）。所提方法简称进化型EELM-MRFO-LF，应用于二元体系中纯组分基态晶体结构对应化合物的未弛豫与弛豫态形成能预测。EELM-MRFO-LF遵循传统进化型ELM的学习流程：首先使用带莱维飞行的MRFO选择输入权重，再通过摩尔-彭罗斯广义逆（MP）解析求解输出权重。莱维飞行轨迹增强了ELM种群多样性，有效防止早熟收敛并避免陷入局部最优。所提方法在相似条件下与多种经典自然启发算法进行了对比，验证了其优越性。
原文摘要 · Abstract (English)
The Manta Ray Foraging Optimization algorithm (MRFO) has proven to be a powerful heuristic strategy in the optimal solution of a large number of engineering problems. In this paper, an improvement of MRFO with Levy Flight is suggested for the training of extreme learning machines (ELMs) whose basic model is a Single Layer Feedforward Network (SLFN). The proposed methodology that we called Evolutionary EELM-MRFO-LF for short is implemented to the prediction of unrelaxed and relaxed formation energy compounds relative to ground state crystal structure of pure components in binary systems. EELM-MRFO-LF follows the learning procedure of traditional Evolutionary ELMs in which first MRFO with LF is used to select the input weights and Moore-Penrose (MP) generalized inverse is applied to analytically determine the output weights. Levy Flight trajectory is implemented for increasing the diversity of the population of ELMs against premature convergence and the ability of avoiding getting trapped in a local optima. The performance of the suggested EELM-MRFO-LF is compared with other well-known nature-inspired algorithms under similar conditions.
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