發(fā)布時(shí)間：2018-06-16 19:17

  本文選題：群體智能算法 + 人工蜂群算法��； 參考：《江西理工大學(xué)》2017年碩士論文

【摘要】：經(jīng)濟(jì)訂貨批量(Economy Order Quantity,EOQ)是通過平衡各種成本核算使得庫存總成本最低的訂貨量。經(jīng)濟(jì)訂貨批量的計(jì)算過程中,需要估計(jì)訂單的數(shù)量以求得更加準(zhǔn)確的結(jié)果。通過支持向量機(jī)(Support Vector Machine,SVM)能夠?qū)�過往的訂單數(shù)額進(jìn)行計(jì)算,并預(yù)測之后訂單數(shù)額,進(jìn)而求得經(jīng)濟(jì)訂貨批量的數(shù)值。因此為使得支持向量機(jī)的學(xué)習(xí)效果更加準(zhǔn)確,優(yōu)化支持向量機(jī)的方法現(xiàn)已成為熱點(diǎn)研究問題之一。人工蜂群算法是一種模擬蜜蜂采蜜行為的群體智能優(yōu)化算法,由于它具有控制參數(shù)少、易于實(shí)現(xiàn)、計(jì)算簡單、魯棒性強(qiáng)等優(yōu)點(diǎn),處理包括優(yōu)化支持向量機(jī)在內(nèi)的優(yōu)化問題時(shí)有著優(yōu)異的表現(xiàn),已被越來越多的研究者所關(guān)注。人工蜂群算法主要存在兩個(gè)缺點(diǎn):算法特別在處理復(fù)雜的優(yōu)化問題時(shí)容易陷入局部最優(yōu)和過早收斂;算法的探索能力較好,但開發(fā)能力不足,收斂速度較慢。本文從多個(gè)角度對人工蜂群算法進(jìn)行改進(jìn),提高其在處理復(fù)雜優(yōu)化問題方面的尋優(yōu)性能,并在此基礎(chǔ)上,將算法應(yīng)用于優(yōu)化支持向量機(jī)以預(yù)測經(jīng)濟(jì)訂貨批量模型中的訂單預(yù)測問題。本文的研究內(nèi)容主要包括以下兩個(gè)方面:一方面,為提高算法的優(yōu)化精度、局部搜索能力,基于現(xiàn)有的名為Bare-bones ABC和HBC的人工蜂群算法的改進(jìn)算法,提出了一種混合的Bare-bones人工蜂群算法(Hybrid Bare-bones Artificial Bee Colony Algorithm,HBABC)。算法主要改進(jìn)了以下兩個(gè)方面:針對算法容易陷入局部最優(yōu)的方面,引入了HBC算法啟發(fā)自模擬退火算法的特性對蜜源更新的模型進(jìn)行了改進(jìn);針對算法的收斂性不足的方面,通過啟發(fā)自Bare-bones ABC的傾向較優(yōu)個(gè)體進(jìn)行搜索的特性對跟隨蜂選擇雇傭蜂的方式進(jìn)行改進(jìn)。算法通過上述兩個(gè)改進(jìn)以提高收斂精度和優(yōu)化速度。通過使用10個(gè)測試函數(shù)進(jìn)行了對比實(shí)驗(yàn),驗(yàn)證了改進(jìn)算法的有效性。另一方面,本文將HBABC算法用于優(yōu)化支持向量機(jī)的兩個(gè)參數(shù),并將優(yōu)化結(jié)果用于解決現(xiàn)有的實(shí)際問題——基于經(jīng)濟(jì)訂貨批量模型的訂單數(shù)額及金額的擬合和預(yù)測問題。實(shí)驗(yàn)結(jié)果表明,使用HBABC算法優(yōu)化的支持向量機(jī)得到的擬合和預(yù)測結(jié)果總體上比使用ABC和BBABC優(yōu)化的支持向量機(jī)表現(xiàn)更加準(zhǔn)確。
[Abstract]:Economic order quantity EOQ is the lowest order quantity by balancing all kinds of cost accounting. It is necessary to estimate the quantity of order in order to obtain more accurate results. Through support vector machine support Vector Machine (SVM), we can calculate the amount of order in the past and predict the amount of order after that, and then get the value of economic order batch. Therefore, in order to make the learning effect of SVM more accurate, the method of optimizing SVM has become one of the hot research issues. Artificial bee colony algorithm is a swarm intelligence optimization algorithm to simulate honeybee honey gathering behavior. It has the advantages of less control parameters, easy to implement, simple calculation, strong robustness, and so on. More and more researchers have paid attention to the excellent performance of optimization problems including optimization support vector machines (SVM). The artificial bee colony algorithm has two main disadvantages: the algorithm is prone to fall into local optimum and premature convergence especially when dealing with complex optimization problems, and the algorithm has better exploring ability, but the development ability is insufficient, and the convergence speed is slow. In this paper, the artificial bee colony algorithm is improved from several angles to improve its optimization performance in dealing with complex optimization problems, and on this basis, The algorithm is applied to the optimal support vector machine (SVM) to predict the order forecasting problem in the economic order batch model. The research contents of this paper mainly include the following two aspects: on the one hand, in order to improve the optimization accuracy and local search ability of the algorithm, the improved algorithm based on the existing artificial bee colony algorithm named Bare-bones ABC and HBC is proposed. A hybrid Bare-bones Artificial Bee colony algorithm is proposed. The algorithm mainly improves the following two aspects: to solve the problem that the algorithm is easy to fall into local optimum, the characteristic of self-simulated annealing algorithm inspired by HBC algorithm is introduced to improve the model of honey source update, and the convergence of the algorithm is insufficient. By heuristic Bare-bones ABC, which tends to search better individuals, the method of choosing employment bees is improved. The algorithm improves the convergence accuracy and optimization speed through the above two improvements. The effectiveness of the improved algorithm is verified by 10 test functions. On the other hand, the HBABC algorithm is used to optimize the two parameters of support vector machine, and the optimization results are used to solve the existing practical problems-the fitting and forecasting of the amount and amount of orders based on the economic order batch model. The experimental results show that the performance of SVM optimized by HBABC algorithm is more accurate than that of SVM optimized by ABC and BBABC.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F274;TP18

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