本文關(guān)鍵詞：風(fēng)力驅(qū)動(dòng)優(yōu)化算法及其應(yīng)用研究　出處：《廣西民族大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)力驅(qū)動(dòng)優(yōu)化 復(fù)數(shù)編碼 量子編碼 0-1背包問題 無人機(jī)航路規(guī)劃問題 啟發(fā)式算法

【摘要】：風(fēng)力驅(qū)動(dòng)優(yōu)化算法是一種新的啟發(fā)式算法,是由Z.Bayraktar等人在2010年提出。風(fēng)力驅(qū)動(dòng)優(yōu)化算法的思想是模擬自然現(xiàn)象由于各地區(qū)氣壓不同而導(dǎo)致空氣流動(dòng),最終達(dá)到氣壓平衡的過程。在大氣中,空氣的流動(dòng)是為了嘗試去平衡氣壓。當(dāng)氣壓失衡時(shí),空氣將受到氣壓梯度力,繼而以一定的速度從高壓地區(qū)向低壓地區(qū)流動(dòng),最終使氣壓達(dá)到平衡。由于風(fēng)力驅(qū)動(dòng)優(yōu)化算法具有結(jié)構(gòu)簡單、控制變量少、易于理解和實(shí)現(xiàn)等優(yōu)點(diǎn),算法自提出以來受到了越來越多的學(xué)者關(guān)注。但該算法也存在著前期收斂速度過快,易陷入局部最優(yōu),后期種群多樣性不足導(dǎo)致收斂速度慢等缺陷,極大地限制了風(fēng)力驅(qū)動(dòng)優(yōu)化算法的應(yīng)用范圍。因此,風(fēng)力驅(qū)動(dòng)優(yōu)化算法無論是在理論方面,還是在應(yīng)用方面,都有待于進(jìn)一步的研究和擴(kuò)展。本文針對(duì)風(fēng)力驅(qū)動(dòng)優(yōu)化算法存在的不足進(jìn)行分析,從更新策略和編碼方法等方面對(duì)算法進(jìn)行改進(jìn),并將改進(jìn)后的算法應(yīng)用到實(shí)際優(yōu)化問題中。工作內(nèi)容主要包括三個(gè)方面:(1)采取雙種群策略對(duì)風(fēng)力驅(qū)動(dòng)優(yōu)化算法進(jìn)行改進(jìn),其中,一個(gè)種群由風(fēng)力驅(qū)動(dòng)優(yōu)化算法進(jìn)行更新,另一個(gè)種群由差分進(jìn)化算法進(jìn)行更新,兩個(gè)算法通過信息共享機(jī)制實(shí)現(xiàn)種群的共同進(jìn)化。該策略能夠增加種群多樣性,繼而增強(qiáng)算法的全局搜索能力,避免算法因收斂速度過快而陷入局部最優(yōu)。(2)通過改變個(gè)體的編碼方式來改進(jìn)風(fēng)力驅(qū)動(dòng)優(yōu)化算法的性能。將復(fù)數(shù)編碼的思想應(yīng)用到風(fēng)力驅(qū)動(dòng)優(yōu)化算法中,利用實(shí)部和虛部兩個(gè)變量來表示一個(gè)自變量。由于每個(gè)復(fù)數(shù)都可以表達(dá)二維信息,這樣能增強(qiáng)種群表示的信息量和個(gè)體的多樣性。在復(fù)數(shù)編碼風(fēng)力驅(qū)動(dòng)優(yōu)化算法的基礎(chǔ)上引入貪心策略,有效地解決0-1背包問題。(3)將量子編碼理論的思想應(yīng)用到風(fēng)力驅(qū)動(dòng)優(yōu)化算法中,提出了一種量子風(fēng)力驅(qū)動(dòng)優(yōu)化算法。利用量子旋轉(zhuǎn)門實(shí)現(xiàn)種群的更新,利用量子非門策略實(shí)現(xiàn)種群個(gè)體的變異。這兩個(gè)策略能夠提高種群的多樣性,避免種群過早收斂。將量子風(fēng)力驅(qū)動(dòng)優(yōu)化算法應(yīng)用于無人機(jī)航路規(guī)劃,表明了算法的有效性及可行性。
[Abstract]:Wind driven optimization algorithm is a new heuristic algorithm. In 2010, Z. Bayraktar et al., the idea of wind driven optimization algorithm is to simulate the air flow caused by the different air pressure in different regions. The process of eventually reaching a barometric equilibrium. In the atmosphere, air flows in an attempt to balance the pressure. When the pressure is out of balance, the air is subjected to a pressure gradient. Then flow from high pressure region to low pressure area at a certain speed, and finally make the pressure balance. Because of the advantages of simple structure, less control variables, easy to understand and realize, wind driven optimization algorithm has the advantages of simple structure, less control variables, and easy to understand and realize. Since the algorithm was put forward, more and more scholars have paid attention to it. However, the algorithm also has some shortcomings such as the early convergence speed is too fast, it is easy to fall into local optimum, and the late population diversity is insufficient, resulting in the slow convergence rate and so on. The application of wind driven optimization algorithm is greatly limited. Therefore, wind driven optimization algorithm is not only in theory but also in application. This paper analyzes the shortcomings of the wind-driven optimization algorithm and improves the algorithm from the aspects of update strategy and coding method. And the improved algorithm is applied to the practical optimization problem. The main work includes three aspects: 1) adopting the dual-population strategy to improve the wind-driven optimization algorithm. One population is updated by wind driven optimization algorithm and the other population is updated by differential evolution algorithm. The two algorithms realize the coevolution of population through information sharing mechanism. This strategy can increase population diversity. Then the global search ability of the algorithm is enhanced. To avoid falling into local optimum because of the fast convergence speed, the algorithm can improve the performance of wind driven optimization algorithm by changing individual coding method. The idea of complex number coding is applied to wind driven optimization algorithm. Two variables, real part and imaginary part, are used to represent an independent variable. In this way, the amount of information and the diversity of individuals can be enhanced, and the greedy strategy is introduced on the basis of the complex coded wind-driven optimization algorithm. Effectively solve the 0-1 knapsack problem. (3) the quantum coding theory is applied to the wind driven optimization algorithm, and a quantum wind driven optimization algorithm is proposed, which uses the quantum rotary gate to update the population. The two strategies can improve population diversity and avoid premature convergence. Quantum wind driven optimization algorithm is applied to UAV route planning. The effectiveness and feasibility of the algorithm are demonstrated.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 李枝勇;馬良;張惠珍;;函數(shù)優(yōu)化的量子蝙蝠算法[J];系統(tǒng)管理學(xué)報(bào);2014年05期

2 陳得寶;李淮江;李崢;;復(fù)數(shù)編碼粒子群算法及在函數(shù)優(yōu)化中的應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2009年10期

3 李士勇;李盼池;;求解連續(xù)空間優(yōu)化問題的量子粒子群算法[J];量子電子學(xué)報(bào);2007年05期

4 賀毅朝;劉坤起;張翠軍;張巍;;求解背包問題的貪心遺傳算法及其應(yīng)用[J];計(jì)算機(jī)工程與設(shè)計(jì);2007年11期

5 鄭朝暉,張焱,裘聿皇;一種基于復(fù)數(shù)編碼的遺傳算法[J];控制理論與應(yīng)用;2003年01期

，

本文編號(hào)：1366403