【摘要】：群體智能優(yōu)化算法屬于隨機(jī)搜索算法的一種,由于其可以解決傳統(tǒng)優(yōu)化技術(shù)無(wú)法解決的優(yōu)化問(wèn)題,因此受到眾多專家學(xué)者們的青睞。人工蜂群算法是新興的群體智能優(yōu)化算法,算法主要模擬自然界中蜂群覓食的行為機(jī)制來(lái)獲取問(wèn)題的最優(yōu)解。由于人工蜂群算法具有設(shè)置參數(shù)少、計(jì)算簡(jiǎn)單、并行性好、魯棒性強(qiáng)等優(yōu)點(diǎn),在處理優(yōu)化問(wèn)題時(shí)有著良好的優(yōu)化效果,受到了國(guó)內(nèi)外眾多專家和學(xué)者們的重視。雖然人工蜂群算法處理優(yōu)化問(wèn)題時(shí)有諸多優(yōu)點(diǎn),但是算法仍然存在著易陷入局部最優(yōu)解、過(guò)早收斂等問(wèn)題。特別是人工蜂群算法在處理復(fù)雜高維度優(yōu)化和大規(guī)模優(yōu)化問(wèn)題時(shí),超長(zhǎng)的算法運(yùn)行時(shí)間難以接受。因此,為解決這些問(wèn)題,很多研究學(xué)者著手研究人工蜂群算法的并行化算法,即通過(guò)并行化人工蜂群算法來(lái)提高算法的運(yùn)行效率和優(yōu)化精度是非常有必要的。人工蜂群算法的并行化可以用多種并行化的方法來(lái)實(shí)現(xiàn),目前國(guó)內(nèi)外的學(xué)者主要采用多集群MPI(Message Passing Interface)技術(shù)或者采用單機(jī)Java多線程技術(shù)來(lái)實(shí)現(xiàn)粗粒度的并行人工蜂群算法。在公開(kāi)發(fā)表的論文中,他們指出,在處理高維度優(yōu)化問(wèn)題時(shí),人工蜂群算法的主要時(shí)間消耗在計(jì)算適應(yīng)度函數(shù)上,這是研究人工蜂群算法并行化技術(shù)的研究重點(diǎn)和難點(diǎn)。因此,本文在探討了并行化技術(shù)后,利用OpenMP(Open Multi-Processing)多線程技術(shù)和規(guī)約機(jī)制,并結(jié)合已改進(jìn)的觀察蜂選擇雇傭蜂的方式,提出了一種PCABC(Parallel Changed-in-selection-mode Artificial Bee Colony algorithm)算法,來(lái)解決這一難題。PCABC算法采用主從并行模型和共享內(nèi)存的OpenMP方法,對(duì)計(jì)算耗時(shí)多的計(jì)算適應(yīng)度函數(shù)部分做并行處理,使得算法在處理高維度優(yōu)化問(wèn)題的處理時(shí)間大大縮短。本文實(shí)驗(yàn)部分在三種不同的調(diào)度方式下進(jìn)行,分別對(duì)標(biāo)準(zhǔn)人工蜂群算法和并行化的人工蜂群算法進(jìn)行仿真測(cè)試。實(shí)驗(yàn)結(jié)果表明,對(duì)人工蜂群算法適應(yīng)度函數(shù)進(jìn)行并行化改造的PCABC算法,可以大大加快算法處理高維度優(yōu)化問(wèn)題時(shí)的處理時(shí)間,加快算法的收斂速度,實(shí)驗(yàn)達(dá)到了預(yù)期目標(biāo)。在證明PCABC并行人工蜂群算法的有效性之后,本文將其應(yīng)用到解決高維函數(shù)優(yōu)化問(wèn)題和流域水文模型參數(shù)優(yōu)化問(wèn)題上。參數(shù)優(yōu)化對(duì)水文模型整體性能和水文預(yù)報(bào)結(jié)果有著至關(guān)重要的影響。模型參數(shù)優(yōu)化中存在大量的計(jì)算密集型任務(wù),需要耗費(fèi)大量的CPU處理時(shí)間,從而導(dǎo)致模型運(yùn)行效率低下。因此,本文將PCABC算法應(yīng)用于新安江二水源模型來(lái)優(yōu)化率定水文模型。實(shí)驗(yàn)結(jié)果表明,PCABC算法能夠顯著提高水文模型的參數(shù)優(yōu)化效率和精度,同時(shí)具有并行成本低廉、實(shí)現(xiàn)過(guò)程簡(jiǎn)單等優(yōu)點(diǎn)。PCABC算法在水文模型參數(shù)優(yōu)化中的表現(xiàn)優(yōu)異,是求解水文模型參數(shù)優(yōu)化問(wèn)題的一種有效可行的方法。
[Abstract]:Swarm intelligence optimization algorithm is one of the random search algorithms. Because it can solve the optimization problem which can not be solved by traditional optimization technology, it is favored by many experts and scholars. Artificial bee colony algorithm is a new swarm intelligence optimization algorithm. It mainly simulates the behavior mechanism of swarm foraging in nature to obtain the optimal solution of the problem. Artificial bee colony algorithm has many advantages, such as less setting parameters, simple calculation, good parallelism and strong robustness, so it has a good optimization effect when dealing with optimization problems, and has been paid attention to by many experts and scholars at home and abroad. Although artificial bee colony algorithm has many advantages in dealing with optimization problems, it still has problems such as easy to fall into local optimal solution and premature convergence. Especially when artificial bee colony algorithm is used to deal with complex high-dimensional optimization and large-scale optimization, the long running time of the algorithm is difficult to accept. Therefore, in order to solve these problems, many researchers have begun to study the parallel algorithm of artificial bee colony algorithm, that is, it is necessary to improve the efficiency and optimization accuracy of the algorithm by parallelizing artificial bee colony algorithm. The parallelization of artificial bee colony algorithm can be realized by a variety of parallelization methods. At present, scholars at home and abroad mainly adopt multi-cluster MPI (Message Passing Interface) technology or single-machine Java multi-thread technology to realize coarse-grained parallel artificial bee colony algorithm. In their published papers, they point out that the main time of artificial bee colony algorithm is the calculation of fitness function when dealing with high dimensional optimization problem, which is the focus and difficulty of research on parallelization of artificial bee colony algorithm. Therefore, after discussing the parallelization technology, using OpenMP (Open Multi-Processing multithreading technology and protocol mechanism, and combining with the improved way of observing bees choosing employment bees, this paper proposes a PCABC (Parallel Changed-in-selection-mode Artificial Bee Colony algorithm) algorithm. To solve this problem. PCABC algorithm adopts master-slave parallel model and OpenMP method of shared memory, and does parallel processing to the part of the computation fitness function which is time-consuming, so that the processing time of the algorithm in dealing with the high-dimensional optimization problem is greatly shortened. In this paper, three different scheduling methods are used to simulate the standard artificial bee colony algorithm and the parallel artificial bee colony algorithm. The experimental results show that the PCABC algorithm, which parallelizes the fitness function of artificial bee colony algorithm, can greatly speed up the processing time and convergence speed of the algorithm in dealing with high-dimensional optimization problems. After proving the validity of PCABC parallel artificial bee colony algorithm, this paper applies it to solve the problem of high-dimensional function optimization and the optimization of hydrological model parameters. Parameter optimization plays an important role in the overall performance of hydrological model and hydrological prediction results. There are a lot of computationally intensive tasks in the optimization of model parameters, which requires a lot of CPU processing time, which leads to the inefficient operation of the model. Therefore, the PCABC algorithm is applied to the Xinanjiang two-water model to optimize the rate-determined hydrological model. The experimental results show that the PCABC algorithm can significantly improve the efficiency and accuracy of the hydrological model parameter optimization, and it has the advantages of low parallel cost and simple process. PCABC algorithm performs well in the hydrological model parameter optimization. It is an effective and feasible method to solve the optimization problem of hydrological model parameters.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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