發(fā)布時(shí)間：2018-06-18 08:44

  本文選題：城市公交路線問(wèn)題 + 群智能�。� 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：隨著城市化的發(fā)展,城市人口和車輛也越來(lái)越多,不管在世界的哪個(gè)城市,交通堵塞現(xiàn)象發(fā)生的都越來(lái)越頻繁。擁擠的交通給人們生活帶來(lái)很多不便,如人們?cè)诼飞匣ㄙM(fèi)的時(shí)間增多、交通事故發(fā)生的幾率增大,大氣污染越發(fā)嚴(yán)重等。所以各個(gè)城市均亟需一個(gè)可以給人們生活帶來(lái)便利的高效交通系統(tǒng),同時(shí)設(shè)計(jì)一個(gè)高效的公交路線對(duì)于運(yùn)營(yíng)商和當(dāng)?shù)卣�府�?lái)說(shuō)也是一件具有現(xiàn)實(shí)意義的事情。但是城市公交路線設(shè)計(jì)問(wèn)題是一個(gè)很難求得最優(yōu)解的NP難問(wèn)題,同時(shí)也是運(yùn)籌學(xué)領(lǐng)域和組合優(yōu)化領(lǐng)域的熱點(diǎn)研究問(wèn)題,具有很強(qiáng)的現(xiàn)實(shí)研究意義。城市公交路線設(shè)計(jì)問(wèn)題在一定程度上可以抽象為經(jīng)典的組合優(yōu)化問(wèn)題-旅行商問(wèn)題(TSP),所以近年來(lái)許多研究者紛紛利用求解TSP問(wèn)題的方法,如群智能優(yōu)化算法,對(duì)城市公交路線設(shè)計(jì)問(wèn)題進(jìn)行求解。狼群算法作為一種模擬狼群捕食行為的新興群智能優(yōu)化算法,具有尋優(yōu)精度高,收斂速度快和魯棒性強(qiáng)等優(yōu)點(diǎn),所以一經(jīng)提出便受到眾多研究者的關(guān)注。但是狼群算法也存在一些自身的缺陷,如算法復(fù)雜,參數(shù)過(guò)多等。本文主要對(duì)狼群算法進(jìn)行了改進(jìn),簡(jiǎn)化了狼群算法的流程和控制參數(shù)從而提出了一種基于精英策略的改進(jìn)狼群算法,并利用該算法對(duì)經(jīng)典組合優(yōu)化問(wèn)題TSP問(wèn)題進(jìn)行了求解。然后在認(rèn)真研究城市公交路線設(shè)計(jì)問(wèn)題之后提出了更關(guān)注乘客乘車感受的人性化模型,并將改進(jìn)的狼群算法應(yīng)用于城市公交路線設(shè)計(jì)優(yōu)化問(wèn)題中。本文的主要研究工作概述如下:1、針對(duì)狼群算法過(guò)程復(fù)雜難以理解和控制參數(shù)過(guò)多等缺點(diǎn),本文將狼群算法的召喚行為和圍攻行為抽象為一種聚集行為,因?yàn)檫@兩種行為本質(zhì)上都是讓狼群中的其他個(gè)體向最優(yōu)個(gè)體靠攏。這樣不僅簡(jiǎn)化了原始狼群算法的過(guò)程而且還去掉了狼群算法中的圍攻步長(zhǎng)和奔襲步長(zhǎng)等參數(shù),減少了算法的控制參數(shù)。2、利用改進(jìn)的狼群算法求解TSP問(wèn)題,在求解過(guò)程中本文提出了一種新的局部?jī)?yōu)化算子即聚集優(yōu)化算子。該算子是基于2-opt算子實(shí)現(xiàn)的,并在狼群的聚集行為中對(duì)解序列進(jìn)行優(yōu)化,而在狼群的游走行為中主要通過(guò)2-opt算子對(duì)解序列進(jìn)行優(yōu)化。為了證明該算法在求解TSP問(wèn)題中的有效性,本文對(duì)TSPLIB庫(kù)中的12個(gè)數(shù)據(jù)集進(jìn)行了仿真實(shí)驗(yàn),并將實(shí)驗(yàn)結(jié)果與文獻(xiàn)中的其他8種算法進(jìn)行對(duì)比。3、提出了一種更加關(guān)注乘客乘車感受的城市公交路線設(shè)計(jì)模型,該模型不僅考慮了乘客的乘車時(shí)間、轉(zhuǎn)車時(shí)間、轉(zhuǎn)車次數(shù)還考慮了轉(zhuǎn)乘給乘客帶來(lái)的煩感。針對(duì)該模型本文設(shè)計(jì)了利用改進(jìn)狼群算法求解城市公交路線設(shè)計(jì)問(wèn)題的具體實(shí)現(xiàn)方法,主要包括路線初始化、狼群算法的游走行為和聚集行為。最后本文在Mandl交通網(wǎng)絡(luò)上進(jìn)行了仿真實(shí)驗(yàn),并分別對(duì)4條路線、6條路線、7條路線和8條路線的情況進(jìn)行了討論,并將實(shí)驗(yàn)結(jié)果與文獻(xiàn)中的其他13種算法進(jìn)行了對(duì)比,結(jié)果證明了算法的可行性和有效性。
[Abstract]:With the development of urbanization, there are more and more urban population and vehicles, no matter which city in the world, traffic jams occur more and more frequently. The crowded traffic brings a lot of inconvenience to people's life, such as the increase of time spent on the road, the increase of the probability of traffic accidents, the more serious the air pollution and so on. Therefore, every city needs an efficient transportation system which can bring convenience to people's life, and it is also of practical significance to design an efficient bus route for operators and local governments. However, the problem of urban bus route design is a NP-hard problem which is difficult to find the optimal solution, and it is also a hot research problem in the field of operational research and combinatorial optimization, which has a strong practical significance. To some extent, the urban bus route design problem can be abstracted as a classical combinatorial optimization problem-traveling salesman problem (TSPP). So in recent years, many researchers have used methods to solve tsp, such as swarm intelligence optimization algorithm. The problem of urban bus route design is solved. As a new intelligent optimization algorithm for simulating predation behavior of wolves, wolf swarm algorithm has many advantages, such as high precision, fast convergence and strong robustness, so it has attracted many researchers' attention once it is proposed. But the wolf swarm algorithm also has some defects, such as complex algorithm, too many parameters and so on. In this paper, we improve the wolf swarm algorithm, simplify the flow and control parameters of the algorithm, and then propose an improved wolf swarm algorithm based on elitist strategy, and use this algorithm to solve the tsp problem of the classical combinatorial optimization problem. Then the humanized model of paying more attention to the passenger's experience is put forward after studying the problem of urban bus route design seriously, and the improved wolf swarm algorithm is applied to the optimization problem of urban bus route design. The main research work of this paper is summarized as follows: 1. In view of the complexity of the wolf swarm algorithm and the complexity of control parameters, this paper abstracts the call behavior and besieging behavior of the wolf swarm algorithm as a kind of aggregation behavior. Because both actions essentially bring the rest of the pack closer to the optimal individual. This not only simplifies the process of the original wolf swarm algorithm, but also removes the parameters such as the besieged step size and the running step size of the wolf swarm algorithm, reduces the control parameter of the algorithm, and uses the improved wolf swarm algorithm to solve the tsp problem. In this paper, a new local optimization operator, aggregative optimization operator, is proposed. This operator is based on the 2-opt operator and optimizes the solution sequence in the aggregation behavior of the wolves, while the 2-opt operator is used to optimize the solution sequence in the walk behavior of the wolves. In order to prove the effectiveness of the algorithm in solving tsp problem, this paper makes a simulation experiment on 12 datasets in TSPLIB database. By comparing the experimental results with the other eight algorithms in the literature, a new urban bus route design model is proposed, which not only considers the passenger's travel time and transit time, but also puts forward a city bus route design model, which pays more attention to the passenger's experience. The number of transfers also takes into account the annoyance of passengers. According to the model, this paper designs a method to solve the problem of urban bus route design using improved wolf swarm algorithm, which mainly includes route initialization, walk-away behavior and aggregation behavior of wolf swarm algorithm. Finally, the simulation experiments are carried out on Mandl traffic network, and the cases of 4 routes, 6 routes, 7 routes and 8 routes are discussed, and the experimental results are compared with the other 13 algorithms in the literature. The results show that the algorithm is feasible and effective.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.17;TP18

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