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(guó)水電事業(yè)日益發(fā)展,越來(lái)越多的水電站不斷被開(kāi)發(fā)利用,梯級(jí)水庫(kù)群被廣泛應(yīng)用于各級(jí)水利樞紐系統(tǒng)。如何對(duì)梯級(jí)水庫(kù)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(jí)水庫(kù)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(jià)值。水庫(kù)調(diào)度是高維、多時(shí)段的非線性優(yōu)化問(wèn)題。傳統(tǒng)算法通過(guò)建立精確模型的方式能夠解決單一水庫(kù)的調(diào)度問(wèn)題,但隨著水庫(kù)數(shù)目的增多,優(yōu)化問(wèn)題的計(jì)算量顯著增大,造成“維度災(zāi)”,難以符合實(shí)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(wèn)題,這為解決梯級(jí)水庫(kù)群調(diào)度問(wèn)題提供了新的途徑。人工蜂群算法具有結(jié)構(gòu)簡(jiǎn)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(duì)象,并對(duì)其進(jìn)行改進(jìn),取得主要成果如下:(1)針對(duì)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(duì)其進(jìn)行改進(jìn)。通過(guò)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(kāi)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(shí),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(shí)將整個(gè)種群隨機(jī)劃分為兩個(gè)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(gè)個(gè)體的信息,在蜜源進(jìn)化的同時(shí)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(wèn)題,采用多群策略對(duì)算法進(jìn)行優(yōu)化。首先,將雇傭蜂隨機(jī)分為三個(gè)子群,分別對(duì)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(kāi)發(fā)能力。其次,通過(guò)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(guò)12個(gè)經(jīng)典基準(zhǔn)函數(shù)和28個(gè)CEC2013函數(shù)測(cè)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(jí)水庫(kù)群(水布婭—隔河巖—高壩洲)為研究背景,以梯級(jí)水電站總發(fā)電量最大為目標(biāo)函數(shù),建立梯級(jí)水庫(kù)群聯(lián)合調(diào)度模型,將三種算法應(yīng)用于梯級(jí)水庫(kù)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TV697.12;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馮仲愷;廖勝利;牛文靜;程春田;唐建興;蘇華英;;梯級(jí)水電站群中長(zhǎng)期優(yōu)化調(diào)度的正交離散微分動(dòng)態(tài)規(guī)劃方法[J];中國(guó)電機(jī)工程學(xué)報(bào);2015年18期

2 孫輝;朱德剛;王暉;趙嘉;;自適應(yīng)子空間高斯學(xué)習(xí)的粒子群優(yōu)化算法[J];南昌工程學(xué)院學(xué)報(bào);2015年04期

3 趙輝;李牧東;翁興偉;;具有自適應(yīng)全局最優(yōu)引導(dǎo)快速搜索策略的人工蜂群算法[J];控制與決策;2014年11期

4 涂瑩;舒丹丹;張銀行;;動(dòng)態(tài)差分進(jìn)化算法在梯級(jí)水庫(kù)優(yōu)化問(wèn)題中的應(yīng)用[J];南水北調(diào)與水利科技;2014年04期

5 孟憲臣;郭立俠;潘豐;;改進(jìn)人工蜂群算法在二維Otsu圖像分割中的應(yīng)用[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2014年06期

6 馬文強(qiáng);唐秋華;張超勇;邵新宇;;基于離散人工蜂群算法的煉鋼連鑄調(diào)度優(yōu)化方法[J];計(jì)算機(jī)集成制造系統(tǒng);2014年03期

7 李文莉;李郁俠;任平安;;基于云變異人工蜂群算法的梯級(jí)水庫(kù)群優(yōu)化調(diào)度[J];水力發(fā)電學(xué)報(bào);2014年01期

8 李俊;孫輝;史小露;;多種群粒子群算法與混合蛙跳算法融合的研究[J];小型微型計(jì)算機(jī)系統(tǒng);2013年09期

9 馬立亞;雷曉輝;蔣云鐘;王浩;;基于DPSA的梯級(jí)水庫(kù)群優(yōu)化調(diào)度[J];中國(guó)水利水電科學(xué)研究院學(xué)報(bào);2012年02期

10 湯可宗;柳炳祥;楊靜宇;孫廷凱;;雙中心粒子群優(yōu)化算法[J];計(jì)算機(jī)研究與發(fā)展;2012年05期

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