本文選題：多目標(biāo)控制 + 鳥(niǎo)群算法��； 參考：《中國(guó)科學(xué)院大學(xué)（工程管理與信息技術(shù)學(xué)院）》2014年碩士論文

【摘要】：交通信號(hào)控制系統(tǒng)是智能交通系統(tǒng)的基礎(chǔ)子系統(tǒng),能夠協(xié)調(diào)控制區(qū)域內(nèi)交通信號(hào)燈的配時(shí)方案,均衡路網(wǎng)內(nèi)交通流運(yùn)行,充分發(fā)揮道路系統(tǒng)的交通效益。然而,目前交通信號(hào)控制方法單一,靈活性差,不能有效緩解城市復(fù)雜路網(wǎng)的交通問(wèn)題。因此有必要優(yōu)化控制算法,找到具有針對(duì)性的交通信號(hào)控制解決方案。 線性加權(quán)法、目標(biāo)規(guī)劃、約束方法等傳統(tǒng)的多目標(biāo)控制算法將各種客觀功能集成到一個(gè)單一的目標(biāo)函數(shù),通過(guò)決策者或優(yōu)化方法本身設(shè)定的系數(shù)的值自適應(yīng)調(diào)整。這些傳統(tǒng)方法雖然簡(jiǎn)單容易實(shí)現(xiàn),但由于多目標(biāo)控制問(wèn)題的目標(biāo)函數(shù)可能非線性、連續(xù)或不可微,需要事先充分掌握先驗(yàn)對(duì)優(yōu)化問(wèn)題的知識(shí)。因此,這些傳統(tǒng)方法往往無(wú)法解決更復(fù)雜的多目標(biāo)控制問(wèn)題。而相對(duì)于控制系統(tǒng)的傳統(tǒng)優(yōu)化算法,進(jìn)化算法是一種模仿生物自然選擇和進(jìn)化過(guò)程的隨機(jī)搜索算法,更適用于處理多目標(biāo)控制的實(shí)際問(wèn)題。同時(shí),由于鳥(niǎo)群算法思想新穎,且在智能交通控制方面的應(yīng)用研究相對(duì)較少,對(duì)于高維的復(fù)雜問(wèn)題,鳥(niǎo)群算法可以在盡可能降低計(jì)算量的同時(shí)保證較為理想的收斂結(jié)果,既克服了基于梯度的算法不易跳出局部最優(yōu)解的問(wèn)題,又克服了窮舉法計(jì)算量過(guò)于巨大的缺點(diǎn)。因此,通過(guò)對(duì)已有算法進(jìn)行適當(dāng)改進(jìn)后用于智能交通控制,可以取得突破性的進(jìn)展。 本文主要采用鳥(niǎo)群算法進(jìn)行尋優(yōu),首先,通過(guò)研究層次分析法和鳥(niǎo)群算法的相關(guān)理論,重點(diǎn)研究鳥(niǎo)群算法的基本原理、數(shù)學(xué)模型、參數(shù)分析從而提出了針對(duì)交通控制系統(tǒng)參數(shù)尋優(yōu)的改進(jìn)辦法；同時(shí)對(duì)鳥(niǎo)群算法的應(yīng)用進(jìn)行分析,通過(guò)對(duì)已有相關(guān)理論的研究對(duì)比,進(jìn)一步加深對(duì)該算法的認(rèn)識(shí)。其次,在研究鳥(niǎo)群算法基本理論的基礎(chǔ)上,對(duì)基本鳥(niǎo)群算法進(jìn)行改進(jìn),期望能夠避免算法早熟收斂的問(wèn)題,使其性能在基本鳥(niǎo)群算法基礎(chǔ)上能有明顯提高。最后,通過(guò)使用VISSIM交通仿真軟件,完成路網(wǎng)模型的繪制和交通仿真參數(shù)的設(shè)置,并將優(yōu)化改進(jìn)后的鳥(niǎo)群算法用于交通信號(hào)控制的仿真實(shí)驗(yàn),以驗(yàn)證在多目標(biāo)期望下優(yōu)化后的鳥(niǎo)群算法在控制效果方面的效果。 結(jié)合以上研究和試驗(yàn)工作,本文引入多目標(biāo)控制思想,將層次分析法和鳥(niǎo)群尋優(yōu)算法相結(jié)合,用于解決交通信號(hào)燈控制問(wèn)題：通過(guò)采用多目標(biāo)方法,對(duì)指標(biāo)層參數(shù)進(jìn)行控制加權(quán),得到不同目標(biāo)下的交通通暢程度的評(píng)價(jià)函數(shù)；進(jìn)而使用鳥(niǎo)群算法,以可以接受的速度和準(zhǔn)確度優(yōu)化出交通燈信號(hào)參數(shù)。層次分析法的引入使得評(píng)價(jià)函數(shù)更加合理有效,而鳥(niǎo)群算法的運(yùn)用使得在保證尋優(yōu)結(jié)果可靠性的同時(shí)極大地減小計(jì)算量。 總體來(lái)說(shuō),本文主要完成了以下幾個(gè)方面工作： 1、學(xué)習(xí)并研究了層次分析法及鳥(niǎo)群算法的相關(guān)理論,重點(diǎn)研究鳥(niǎo)群算法基本原理、數(shù)學(xué)模型、參數(shù)分析及其改進(jìn)辦法。對(duì)鳥(niǎo)群算法的應(yīng)用進(jìn)行綜述,通過(guò)對(duì)已有相關(guān)理論的研究對(duì)比,進(jìn)一步加深對(duì)該算法的認(rèn)識(shí)。 2、在以上學(xué)習(xí)研究的基礎(chǔ)上,改進(jìn)基本鳥(niǎo)群算法,較為有效的避免算法早熟收斂的問(wèn)題,使其性能比基本鳥(niǎo)群算法有明顯提高。 3、學(xué)習(xí)使用VISSIM交通仿真軟件,完成路網(wǎng)模型的繪制和交通仿真參數(shù)的設(shè)置。 4、論文的最后將優(yōu)化改進(jìn)后的鳥(niǎo)群算法用于交通信號(hào)控制的仿真實(shí)驗(yàn),實(shí)驗(yàn)表明,在多目標(biāo)期望下優(yōu)化鳥(niǎo)群算法在控制效果方面有不錯(cuò)的表現(xiàn)。
[Abstract]:The traffic signal control system is the basic subsystem of the intelligent traffic system. It can coordinate the timing scheme of traffic signal in the region, balance the traffic flow in the road network and give full play to the traffic efficiency of the road system. However, the traffic signal control method is single and the flexibility is poor, and it can not effectively alleviate the traffic problems of the city complex road network. Therefore, it is necessary to optimize the control algorithm and find a targeted solution for traffic signal control.
The traditional Multiobjective Control Algorithms, such as linear weighting, target programming and constraint methods, integrate various objective functions into a single target function and adjust themselves by the values of the coefficients set by the decision-makers or optimization methods. These traditional methods are simple and easy to implement, but the objective function of the multiobjective control problem is possible. Nonlinear, continuous or non differentiable, it is necessary to fully grasp the knowledge of priori optimization problems in advance. Therefore, these traditional methods are often unable to solve more complex multi-objective control problems. Compared with the traditional optimization algorithms of control systems, evolutionary algorithm is a kind of random search algorithm that mimics biological natural selection and evolution process, and it is more applicable. At the same time, because of the novel idea of the bird swarm algorithm and less research on the application of intelligent traffic control, the bird swarm algorithm can reduce the computational complexity as much as possible while guaranteeing more ideal convergence results, which can not only overcome the gradient based algorithm, but also overcome the gradient algorithm. The problem of local optimal solution also overcomes the shortcomings of the immense computation of exhaustion method. Therefore, through the proper improvement of the existing algorithms for intelligent traffic control, breakthrough progress can be achieved.
This paper mainly uses the bird swarm algorithm to optimize. First, by studying the theory of AHP and the bird swarm algorithm, we focus on the basic principle, mathematical model and parameter analysis of the bird swarm algorithm, and propose an improved method for optimizing the parameters of the traffic control system. Secondly, on the basis of the basic theory of bird swarm algorithm, the basic bird swarm algorithm is improved to avoid the premature convergence of the algorithm, so that its performance can be obviously improved on the basis of the basic bird swarm algorithm. Finally, through the use of VISSIM traffic simulation The software has completed the drawing of road network model and the setting of traffic simulation parameters, and the improved bird swarm algorithm is applied to the simulation experiment of traffic signal control to verify the effect of the bird swarm optimization on the control effect after the multi target expectation optimization.
Combined with the above research and experiment work, this paper introduces multi-objective control thought, combines AHP and bird swarm optimization algorithm to solve the problem of traffic signal control: the evaluation function of traffic patency under different targets is obtained by using multi target method, and the evaluation function of traffic patency under different targets is obtained; and then the bird is used. The group algorithm optimizes the traffic light signal parameters with the acceptable speed and accuracy. The introduction of AHP makes the evaluation function more reasonable and effective, and the application of the bird group algorithm greatly reduces the computation while ensuring the reliability of the optimization results.
Generally speaking, this paper mainly completed the following aspects:
1, we study and study the related theories of AHP and bird swarm algorithm, and focus on the basic principle, mathematical model, parameter analysis and improvement methods of bird swarm algorithm. The application of bird swarm algorithm is summarized, and the understanding of the algorithm is further deepened through the comparison of the related theories.
2, on the basis of the study above, the basic bird swarm algorithm is improved, and the premature convergence of the algorithm is effectively avoided, and the performance of the algorithm is obviously improved than the basic bird swarm algorithm.
3, learn to use VISSIM traffic simulation software to complete the road network model drawing and traffic simulation parameters settings.
4, at the end of this paper, the improved bird swarm optimization algorithm is used to simulate the traffic signal control. The experiment shows that the optimal bird swarm algorithm has a good performance in the control effect under the multi target expectation.
【學(xué)位授予單位】：中國(guó)科學(xué)院大學(xué)（工程管理與信息技術(shù)學(xué)院）
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.54

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本文編號(hào)：1918035