本文選題：人工蜂群算法 + 優(yōu)化調(diào)度　； 參考：《南昌工程學(xué)院》2017年碩士論文

【摘要】：隨著我國水電事業(yè)日益發(fā)展,越來越多的水電站不斷被開發(fā)利用,梯級水庫群被廣泛應(yīng)用于各級水利樞紐系統(tǒng)。如何對梯級水庫群進(jìn)行合理調(diào)度,提高整體發(fā)電量成為水力資源管理利用的核心內(nèi)容之一。因此,研究梯級水庫群優(yōu)化調(diào)度,制定調(diào)度規(guī)則,具有十分重要的學(xué)術(shù)意義和應(yīng)用價值。水庫調(diào)度是高維、多時段的非線性優(yōu)化問題。傳統(tǒng)算法通過建立精確模型的方式能夠解決單一水庫的調(diào)度問題,但隨著水庫數(shù)目的增多,優(yōu)化問題的計算量顯著增大,造成“維度災(zāi)”,難以符合實際應(yīng)用。隨著現(xiàn)代人工智能技術(shù)的發(fā)展,大量智能算法被應(yīng)用于解決復(fù)雜的優(yōu)化問題,這為解決梯級水庫群調(diào)度問題提供了新的途徑。人工蜂群算法具有結(jié)構(gòu)簡單、魯棒性強(qiáng)等優(yōu)點(diǎn),被廣泛應(yīng)用于眾多工程領(lǐng)域。但是,該算法本身仍存在許多不足。本文以標(biāo)準(zhǔn)人工蜂群算法為研究對象,并對其進(jìn)行改進(jìn),取得主要成果如下:(1)針對標(biāo)準(zhǔn)人工蜂群算法收斂速度慢的缺點(diǎn),引進(jìn)改進(jìn)粒子群算法中狹義中心的概念,并對其進(jìn)行改進(jìn)。通過比較適應(yīng)度,選取優(yōu)秀的蜜源構(gòu)成改進(jìn)的狹義中心,使狹義中心具有更好的性質(zhì);其次,修改標(biāo)準(zhǔn)蜂群的更新策略,利用全局最優(yōu)解引導(dǎo),使雇傭蜂始終圍繞當(dāng)前全局最優(yōu)點(diǎn)搜索,強(qiáng)化蜂群在最優(yōu)點(diǎn)附近開發(fā)隱藏解的能力,提高算法的求解精度。由此提出一種改進(jìn)狹義中心的人工蜂群算法。(2)在收斂速度提升的同時,算法極易陷入局部最優(yōu),因此引入虛擬蜜源思想。在初始化時將整個種群隨機(jī)劃分為兩個子群,并采取不同的方法建立虛擬蜜源以代替原蜜源進(jìn)化。由于虛擬蜜源擁有多個個體的信息,在蜜源進(jìn)化的同時加強(qiáng)不同子群間的信息交流,達(dá)到綜合學(xué)習(xí)的目的,構(gòu)造了綜合學(xué)習(xí)的人工蜂群算法。(3)為了改變單一進(jìn)化模式導(dǎo)致算法搜索能力失衡的問題,采用多群策略對算法進(jìn)行優(yōu)化。首先,將雇傭蜂隨機(jī)分為三個子群,分別對應(yīng)三種進(jìn)化策略。由于三種策略具有不同的特征,能夠平衡算法的全局搜索與局部開發(fā)能力。其次,通過模仿粒子群算法,充分利用當(dāng)前全局最優(yōu)蜜源和隨機(jī)鄰域蜜源包含的豐富信息,優(yōu)化了跟隨峰的搜索策略。構(gòu)建了改進(jìn)的多策略人工蜂群算法。論文提出了三種改進(jìn)算法。通過12個經(jīng)典基準(zhǔn)函數(shù)和28個CEC2013函數(shù)測試結(jié)果表明,三種算法具有較好的搜索效率和尋優(yōu)精度。最后,論文以清江流域的梯級水庫群(水布婭—隔河巖—高壩洲)為研究背景,以梯級水電站總發(fā)電量最大為目標(biāo)函數(shù),建立梯級水庫群聯(lián)合調(diào)度模型,將三種算法應(yīng)用于梯級水庫發(fā)電調(diào)度中,取得了良好的結(jié)果。
[Abstract]:With the development of hydropower industry in China, more and more hydropower stations are being developed and used. How to carry on the reasonable operation to the cascade reservoir group and how to improve the whole generating quantity become one of the core contents of the management and utilization of the hydraulic resources. Therefore, it is of great academic significance and application value to study the optimal operation of cascade reservoir groups and to formulate dispatching rules. Reservoir operation is a high-dimensional, multi-time nonlinear optimization problem. The traditional algorithm can solve the operation problem of a single reservoir by establishing an accurate model. However, with the increase of the number of reservoirs, the calculation of the optimization problem increases significantly, resulting in a "dimensional disaster", which is difficult to be applied in practice. With the development of modern artificial intelligence technology, a large number of intelligent algorithms are applied to solve complex optimization problems, which provides a new way to solve the cascade reservoir group scheduling problem. Artificial bee colony algorithm is widely used in many engineering fields because of its simple structure and strong robustness. However, the algorithm itself still has many shortcomings. In this paper, we take the standard artificial bee colony algorithm as the research object and improve it. The main results are as follows: 1) aiming at the shortcoming of the standard artificial bee colony algorithm, we introduce the concept of narrow center in the improved particle swarm algorithm. And improve it. Through the comparison of fitness, select the excellent honey source to form the improved narrow center, make the narrow center have better properties. Secondly, modify the renewal strategy of the standard bee colony, use the global optimal solution to guide, The employment bee is always focused on the current global optimal search to enhance the ability of the colony to develop hidden solutions near the best and to improve the accuracy of the algorithm. Therefore, an improved artificial honeybee colony algorithm .Y2 (narrow center) is proposed. It is easy to fall into local optimum when the convergence speed is improved, so the virtual honeycomb is introduced. The whole population is randomly divided into two subgroups in initialization, and different methods are adopted to establish virtual honey source instead of original honey source evolution. Since virtual honey source has more than one individual's information, it strengthens the exchange of information among different subgroups as well as the evolution of honey source, so as to achieve the purpose of comprehensive learning. In order to change the unbalance of search ability caused by a single evolutionary model, a synthetic learning artificial bee colony algorithm is constructed. In order to optimize the algorithm, a multi-swarm strategy is used to optimize the algorithm. First, employment bees were randomly divided into three subgroups, corresponding to three evolutionary strategies. Because the three strategies have different characteristics, they can balance the ability of global search and local development of the algorithm. Secondly, by imitating the particle swarm optimization (PSO) algorithm, the search strategy of the following peak is optimized by making full use of the abundant information contained in the global optimal honey source and the random neighbor honey source. An improved multi-strategy artificial bee colony algorithm is constructed. Three improved algorithms are proposed in this paper. The test results of 12 classical datum functions and 28 CEC2013 functions show that the three algorithms have better search efficiency and optimization accuracy. Finally, taking the cascade reservoir group (Shuibuya, Geheyan and Gaobazhou) in the Qingjiang River Basin as the research background, taking the maximum total generating capacity of the cascade hydropower station as the objective function, the combined operation model of the cascade reservoir group is established. Three algorithms are applied to cascade reservoir power generation operation, and good results are obtained.
【學(xué)位授予單位】：南昌工程學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TV697.12;TP18

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