【摘要】：演化算法是一類(lèi)啟發(fā)于自然現(xiàn)象或規(guī)律的智能搜索和優(yōu)化技術(shù)的總稱(chēng)。由于其高效的優(yōu)化性能和巨大的應(yīng)用潛力,演化算法在過(guò)去的半個(gè)多世紀(jì)受到了國(guó)內(nèi)外研究人員的廣泛關(guān)注。有鑒于此,本文旨在近來(lái)提出的人工雨滴算法的基礎(chǔ)上,對(duì)其在復(fù)雜連續(xù)優(yōu)化問(wèn)題的求解方面展開(kāi)研究。主要工作如下:(1)為進(jìn)一步理解人工雨滴算法的運(yùn)行機(jī)理和計(jì)算效果,首先,利用相關(guān)的數(shù)學(xué)理論,證明人工雨滴算法在變量不相關(guān)的條件下是以概率1收斂到滿(mǎn)意種群;其次,與三個(gè)演化算法在CEC2005測(cè)試平臺(tái)上進(jìn)行優(yōu)化性能比較。實(shí)驗(yàn)結(jié)果證實(shí)了人工雨滴算法在解決復(fù)雜連續(xù)優(yōu)化問(wèn)題的有效性。(2)在利用人工雨滴算法求解多目標(biāo)優(yōu)化問(wèn)題時(shí),如何在算法設(shè)計(jì)過(guò)程中融合問(wèn)題的特征是提高計(jì)算效率的重要方面。為此,提出一種先驗(yàn)驅(qū)動(dòng)多目標(biāo)人工雨滴算法。首先,通過(guò)結(jié)合非支配排序框架和人工雨滴算法搜索引擎,提出一種多目標(biāo)人工雨滴算法;其次,為加快多目標(biāo)人工雨滴算法的收斂速度,通過(guò)集成多目標(biāo)優(yōu)化的先驗(yàn)知識(shí)-搜索空間的中心點(diǎn)和二項(xiàng)交叉算子,來(lái)引導(dǎo)種群快速向理想Pareto前沿靠近;最后,為保持種群選擇的有效性和非支配解的多樣性,提出一種基于最近擁擠距離的非支配解修剪方法。為驗(yàn)證算法的優(yōu)化性能,選取了 12個(gè)多目標(biāo)測(cè)試函數(shù)進(jìn)行驗(yàn)證,并與其它四個(gè)多目標(biāo)優(yōu)化算法進(jìn)行對(duì)比。結(jié)果表明提出的算法比其它的優(yōu)化算法能夠更快地跳出Pareto局部最優(yōu)解,并獲得了更好的Pareto前沿的均勻性。(3)針對(duì)電力系統(tǒng)中的無(wú)功優(yōu)化問(wèn)題,首先,建立以電壓偏差和有功網(wǎng)損為目標(biāo)的多目標(biāo)優(yōu)化模型。其次,利用提出的多目標(biāo)人工雨滴算法進(jìn)行求解,詳細(xì)描述了算法的編碼和流程。最后,在IEEE-30節(jié)點(diǎn)系統(tǒng)進(jìn)行測(cè)試,將優(yōu)化前后的結(jié)果進(jìn)行了對(duì)比,并將結(jié)果與劉佳的文獻(xiàn)中的優(yōu)化結(jié)果進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果表明所用算法實(shí)現(xiàn)了電力系統(tǒng)經(jīng)濟(jì)運(yùn)行的同時(shí),提高了電網(wǎng)的電壓穩(wěn)定性。
[Abstract]:Evolutionary algorithms are a class of intelligent search and optimization techniques inspired by natural phenomena or laws. Because of its high performance and great application potential, evolutionary algorithms have attracted much attention from researchers at home and abroad in the past half century. In view of this, this paper aims to study the solution of complex continuous optimization problems based on the recently proposed artificial raindrop algorithm. The main work is as follows: (1) in order to further understand the operation mechanism and calculation effect of artificial raindrop algorithm, it is proved that the artificial raindrop algorithm converges to a satisfactory population with probability 1 under the condition that the variables are not related to each other. Secondly, the optimization performance is compared with the three evolutionary algorithms on the CEC2005 test platform. Experimental results show that artificial raindrop algorithm is effective in solving complex continuous optimization problems. (2) when artificial raindrop algorithm is used to solve multi-objective optimization problem, How to fuse the characteristics of the problem in the process of algorithm design is an important aspect to improve the computational efficiency. Therefore, a priori driven multi-objective artificial raindrop algorithm is proposed. Firstly, a multi-objective artificial raindrop algorithm is proposed by combining the non-dominated sorting framework with the artificial raindrop algorithm. Secondly, in order to speed up the convergence of the multi-objective artificial raindrop algorithm, by integrating the priori knowledge of multi-objective optimization, the center of search space and the binomial crossover operator, the population is guided to the ideal Pareto front quickly. Finally, in order to maintain the effectiveness of population selection and the diversity of non-dominated solutions, a pruning method based on the nearest crowding distance is proposed. In order to verify the optimization performance of the algorithm, 12 multi-objective test functions are selected for verification, and compared with the other four multi-objective optimization algorithms. The results show that the proposed algorithm can jump out of the Pareto local optimal solution faster than other optimization algorithms, and obtain better uniformity of the Pareto frontier. (3) for reactive power optimization problems in power systems, firstly, A multi-objective optimization model with voltage deviation and active power loss as targets is established. Secondly, the multi-objective artificial raindrop algorithm is used to solve the problem, and the coding and flow of the algorithm are described in detail. Finally, the IEEE-30 node system is tested, the results before and after optimization are compared, and the results are compared with the optimized results in Liu Jia's literature. The experimental results show that the proposed algorithm realizes the economic operation of power system at the same time. The voltage stability of the power network is improved.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 程憲寶;;基于二項(xiàng)式交叉改進(jìn)的人工蜂群算法[J];山東理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年05期

2 宣冬梅;王菊韻;于華;趙佳;;深度學(xué)習(xí)中先驗(yàn)知識(shí)的應(yīng)用[J];計(jì)算機(jī)工程與設(shè)計(jì);2015年11期

3 朱君;蔡延光;湯雅連;楊軍;;多目標(biāo)優(yōu)化問(wèn)題的研究[J];東莞理工學(xué)院學(xué)報(bào);2014年03期

4 張洪波;楊琳;劉金龍;楊德龍;張晨;;基于混沌免疫接種粒子群算法的電力系統(tǒng)多目標(biāo)無(wú)功優(yōu)化[J];黑龍江電力;2011年01期

5 肖斌;薛麗敏;李照順;;對(duì)人工智能發(fā)展新方向的思考[J];信息技術(shù);2009年12期

6 劉佳;李丹;高立群;宋立新;;多目標(biāo)無(wú)功優(yōu)化的向量評(píng)價(jià)自適應(yīng)粒子群算法[J];中國(guó)電機(jī)工程學(xué)報(bào);2008年31期

7 謝濤,陳火旺,康立山;多目標(biāo)優(yōu)化的演化算法[J];計(jì)算機(jī)學(xué)報(bào);2003年08期

8 謝濤,陳火旺;多目標(biāo)優(yōu)化與決策問(wèn)題的演化算法[J];中國(guó)工程科學(xué);2002年02期

相關(guān)會(huì)議論文 前2條

1 陳家照;羅寅生;;群智能優(yōu)化算法研究[A];第三屆中國(guó)智能計(jì)算大會(huì)論文集[C];2009年

2 彭錦;;進(jìn)化算法綜述[A];中國(guó)運(yùn)籌學(xué)會(huì)第六屆學(xué)術(shù)交流會(huì)論文集（上卷）[C];2000年

相關(guān)博士學(xué)位論文 前2條

1 汪云云;結(jié)合先驗(yàn)知識(shí)的分類(lèi)器設(shè)計(jì)研究[D];南京航空航天大學(xué);2012年

2 張利彪;基于粒子群和微分進(jìn)化的優(yōu)化算法研究[D];吉林大學(xué);2007年

相關(guān)碩士學(xué)位論文 前7條

1 李艷麗;基于多目標(biāo)優(yōu)化的粒子群算法研究及其應(yīng)用[D];西南交通大學(xué);2014年

2 勾磐杰;基于先驗(yàn)知識(shí)的輪胎標(biāo)識(shí)識(shí)別方法研究[D];沈陽(yáng)工業(yè)大學(xué);2014年

3 張浪;進(jìn)化動(dòng)態(tài)多目標(biāo)優(yōu)化算法及其應(yīng)用[D];西安電子科技大學(xué);2014年

4 張強(qiáng);基于改進(jìn)遺傳算法的電力系統(tǒng)多目標(biāo)無(wú)功優(yōu)化[D];西安理工大學(xué);2009年

5 宋明曙;電力系統(tǒng)無(wú)功優(yōu)化分析[D];山東大學(xué);2008年

6 朱建才;多目標(biāo)優(yōu)化方法庫(kù)的開(kāi)發(fā)與應(yīng)用研究[D];西北工業(yè)大學(xué);2006年

7 陳（羽中）偉;基于先驗(yàn)知識(shí)的神經(jīng)元網(wǎng)絡(luò)建模與應(yīng)用[D];浙江大學(xué);2002年

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本文編號(hào)：2378209