【摘要】：大數(shù)據(jù)時代的來臨改變了人們生活的方方面面,也為材料科學的發(fā)展帶來了新的機遇和挑戰(zhàn)。隨著材料基因組計劃的提出,材料大數(shù)據(jù)處理研究已經成為一個新的社會熱點。本文就針對材料大數(shù)據(jù)處理中的分子力場優(yōu)化問題進行了深入研究。智能算法又稱為元啟發(fā)式算法,在解決此類問題中有突出表現(xiàn),其中遺傳算法和粒子群算法應用尤為廣泛。隨著社會和科學的發(fā)展,各個領域中的數(shù)據(jù)都越來越多,也越來越復雜,本文要解決的主要問題,分子力場優(yōu)化問題就是一個例子,另外,很多實際應用還對實時性有要求。因此,串行智能算法已經難以滿足應用需求,對之進行并行化研究十分必要。本文在深入分析智能算法本質特征的基礎上,實現(xiàn)了智能算法主要是遺傳算法和粒子群算法的并行化處理。其流程大致為,第一,利用福斯特并行算法設計方法對遺傳算法和粒子群算法進行了特征分析和并行化設計,制定了主從式遺傳算法并行化設計策略和并發(fā)主從混合式粒子群算法并行化設計策略。第二,利用當下流行的OpenMP應用程序接口對遺傳算法和粒子群算法的計算密集部分,即適應度評價函數(shù)進行了并行化處理,實現(xiàn)了智能算法的主從式并行化,使得算法的運算速度隨著所用核數(shù)的增加成比例提升。第三,利用MPI消息傳遞接口混合OpenMP應用程序接口對劃分子群的粒子群算法進行了并行化處理,進一步提升算法執(zhí)行速度的同時,也使算法的優(yōu)化效果得到了很大的提升。其中MPI用于子群信息交互,OpenMP仍然用于適應度函數(shù)并行化處理。另外,針對基于子群劃分的并行化粒子群算法,本文提出一種交流子群個體歷史信息和加入遺傳機制的策略,提升了算法的收斂速度,實驗表明,對于時間復雜度很高的問題,該算法可以達到更好的優(yōu)化效果。最后,以核用輻照碳化硅為例,本文將并行化智能算法應用到了材料大數(shù)據(jù)中的分子力場優(yōu)化問題中。實驗證明,并行化智能算法有更低的時間復雜度和更高的優(yōu)化性能,而且針對此類時間復雜度很高的實際問題,本文提出的加入子群個體歷史信息和遺傳機制的基于子群劃分的并行化粒子群算法有很好的表現(xiàn)。
[Abstract]:The coming of big data has changed all aspects of people's life and brought new opportunities and challenges to the development of material science. With the development of material genome project, the study of material big data has become a new social hotspot. In this paper, the optimization of molecular force field in the treatment of material big data is studied. Intelligent algorithm, also known as meta-heuristic algorithm, has outstanding performance in solving such problems, especially genetic algorithm and particle swarm optimization algorithm. With the development of society and science, the data in various fields are more and more complex. The main problem to be solved in this paper is the optimization of molecular force field. In addition, many practical applications require real-time performance. Therefore, the serial intelligent algorithm has been difficult to meet the application requirements, and it is necessary to research on parallelization. On the basis of analyzing the essential characteristics of intelligent algorithm, this paper realizes the parallel processing of genetic algorithm and particle swarm optimization algorithm. The flow chart is as follows: first, the genetic algorithm and particle swarm optimization algorithm are analyzed and parallelized by using the Foster parallel algorithm design method. The parallel design strategy of master-slave genetic algorithm and concurrent master-slave hybrid particle swarm optimization algorithm is proposed. Secondly, the popular OpenMP application program interface is used to parallelize the computation dense part of genetic algorithm and particle swarm optimization, that is, fitness evaluation function, and realize the master-slave parallelization of intelligent algorithm. The computation speed of the algorithm increases proportionally with the increase of the number of kernels used. Thirdly, the hybrid OpenMP application program interface of MPI messaging interface is used to parallelize the particle swarm optimization algorithm, which further improves the execution speed of the algorithm, and also improves the optimization effect of the algorithm greatly. MPI is used for subgroup information interaction and OpenMP is still used for parallelization of fitness function. In addition, for the parallel particle swarm algorithm based on subgroup partition, this paper proposes a strategy of exchanging historical information of subgroup and adding genetic mechanism, which improves the convergence speed of the algorithm. Experiments show that, for the problem of high time complexity, The algorithm can achieve better optimization effect. Finally, taking nuclear irradiated silicon carbide as an example, the parallel intelligent algorithm is applied to the optimization of molecular force field in big data. Experiments show that the parallel intelligent algorithm has lower time complexity and higher optimization performance. The parallel particle swarm optimization algorithm based on subgroup partition proposed in this paper has a good performance by adding historical information and genetic mechanism of subgroups.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP18;TP311.13

【參考文獻】

相關期刊論文 前7條

1 尹海清;;材料大數(shù)據(jù)助力中國制造創(chuàng)新發(fā)展[J];新材料產業(yè);2015年05期

2 趙繼成;;材料基因組計劃簡介[J];自然雜志;2014年02期

3 劉熱;;OpenMP多核技術研究及其在遺傳算法中的應用[J];沈陽大學學報;2010年05期

4 陳寶國;;并行化遺傳算法研究[J];淮南師范學院學報;2008年03期

5 鄭鋒;李名世;蔡佳佳;;基于OpenMP的并行遺傳算法探討[J];心智與計算;2007年04期

6 郭彤城,慕春棣;并行遺傳算法的新進展[J];系統(tǒng)工程理論與實踐;2002年02期

7 席裕庚，柴天佑，惲為民;遺傳算法綜述[J];控制理論與應用;1996年06期

，

本文編號：2356841