基于改進(jìn)PSO算法的傳感網(wǎng)覆蓋問題研究
發(fā)布時(shí)間：2018-11-09 07:35
【摘要】：近年來，無線傳感器網(wǎng)絡(luò)（Wireless Sensor Network，WSN）技術(shù)的快速發(fā)展使其得到了各行各業(yè)的廣泛關(guān)注。其中，無線傳感器網(wǎng)絡(luò)覆蓋質(zhì)量的優(yōu)劣關(guān)系到整個(gè)系統(tǒng)正常工作時(shí)效率的高低。評價(jià)無線傳感器網(wǎng)絡(luò)覆蓋質(zhì)量的參考標(biāo)準(zhǔn)有很多，如網(wǎng)絡(luò)覆蓋率，網(wǎng)絡(luò)連通性，網(wǎng)絡(luò)能耗性，通信延遲率等。本文以網(wǎng)絡(luò)覆蓋率作為評價(jià)無線傳感器網(wǎng)絡(luò)性能的指標(biāo)，并以提高網(wǎng)絡(luò)覆蓋率為主要目的，對無線傳感器網(wǎng)絡(luò)覆蓋進(jìn)行優(yōu)化。 粒子群優(yōu)化算法（Particle Swarm Optimization，PSO）是Kennedy和Eberhart通過觀察鳥群的群體覓食行為，模擬鳥群覓食過程中帶有擇優(yōu)選擇的信息交互機(jī)制，而提出的一類群集智能計(jì)算方法。PSO算法以其簡單易行的優(yōu)點(diǎn)得到了廣泛的利用，也因其易早熟收斂，搜索精度不高等缺陷，有大量的工作對其進(jìn)行了改進(jìn)處理。 無線傳感器網(wǎng)絡(luò)一般是由大量的傳感器節(jié)點(diǎn)組成，所以無線傳感器網(wǎng)絡(luò)覆蓋優(yōu)化屬于多目標(biāo)優(yōu)化問題。在PSO算法中，粒子群的每個(gè)粒子都攜帶一定的信息，這些信息對應(yīng)無線傳感器網(wǎng)絡(luò)覆蓋優(yōu)化問題的潛在解。而每個(gè)粒子都具有多維度特性，可以用于對應(yīng)部署的傳感器節(jié)點(diǎn)。在粒子的進(jìn)化過程中，較差粒子通過向較優(yōu)粒子學(xué)習(xí)而改善自身的解（即傳感器節(jié)點(diǎn)的部署位置），算法經(jīng)過多次迭代之后最終會得到最優(yōu)解。PSO算法所具有的多粒子多維的特點(diǎn)以及較強(qiáng)的信息交互能力，，使其適合解決無線傳感器網(wǎng)絡(luò)覆蓋優(yōu)化這類多目標(biāo)動態(tài)優(yōu)化問題。 標(biāo)準(zhǔn)PSO算法雖然已具有一定的信息交互能力，但其存在易早熟收斂，優(yōu)化能力較差等問題，所以在解決無線傳感器網(wǎng)絡(luò)覆蓋問題中的優(yōu)化性能并不理想。之后出現(xiàn)的自適應(yīng)PSO算法與VFPSO算法在一定程度上改善了標(biāo)準(zhǔn)PSO算法的缺點(diǎn)，但在優(yōu)化無線傳感器網(wǎng)絡(luò)覆蓋率問題中易早熟收斂的現(xiàn)象仍然制約著覆蓋率的提升。針對早熟收斂問題，本文在自適應(yīng)PSO算法和VFPSO算法的基礎(chǔ)上對二者加以改進(jìn)：在自適應(yīng)PSO算法慣性權(quán)重的進(jìn)化度中加入全體粒子歷史最優(yōu)平均值前后代的比較，使進(jìn)化度的計(jì)算依據(jù)更加全面；在VFPSO算法中引入維度選擇機(jī)制，使隨機(jī)擾動對早熟問題的干預(yù)更加高效。與PSO算法類似，BBO算法（Biogeography-based Optimization，BBO）也是一類具有較強(qiáng)信息交互能力的多目標(biāo)優(yōu)化算法，其改進(jìn)算法（VF-BBO算法）在傳感網(wǎng)覆蓋問題中對覆蓋率的提升效果較好，因此將VF-BBO算法作為兩種改進(jìn)PSO算法的性能對比算法。通過改進(jìn)前后的算法仿真對比，并結(jié)合VF-BBO算法作為覆蓋率優(yōu)化的參考，改進(jìn)后的兩類PSO算法較好的解決了早熟收斂問題，使無線傳感器網(wǎng)絡(luò)覆蓋率提升了5%~15%。
[Abstract]:In recent years, with the rapid development of wireless sensor network (Wireless Sensor Network,WSN) technology, it has received wide attention in various industries. The coverage quality of wireless sensor network is related to the efficiency of the whole system. There are many reference standards for evaluating the coverage quality of wireless sensor networks, such as network coverage, network connectivity, network energy consumption, communication delay rate, etc. In this paper, the coverage of wireless sensor networks is taken as the index to evaluate the performance of wireless sensor networks, and the main purpose of this paper is to improve the coverage of wireless sensor networks to optimize the coverage of wireless sensor networks. Particle Swarm Optimization (Particle Swarm Optimization,PSO) is a mechanism of information exchange between Kennedy and Eberhart, which simulates the selective selection of birds in the process of foraging by observing their foraging behavior. The PSO algorithm has been widely used because of its advantages of simplicity and ease, and has been improved by a great deal of work because of its shortcomings such as premature convergence and low searching accuracy. Wireless sensor networks are generally composed of a large number of sensor nodes, so wireless sensor network coverage optimization is a multi-objective optimization problem. In the PSO algorithm, each particle of the particle swarm carries certain information, which corresponds to the potential solution of the coverage optimization problem in wireless sensor networks. Each particle has multi-dimensional properties and can be used for deploying sensor nodes. In the evolution of particles, poor particles improve their solutions by learning from better particles (that is, the deployment position of sensor nodes). After many iterations, the PSO algorithm has the characteristics of multi-particle multi-dimension and strong information exchange ability, which makes it suitable to solve the multi-objective dynamic optimization problem of wireless sensor network coverage optimization. Although the standard PSO algorithm has some information exchange ability, it has some problems such as premature convergence and poor optimization ability, so the optimization performance is not ideal in solving the coverage problem of wireless sensor networks. The following adaptive PSO algorithm and VFPSO algorithm improve the shortcomings of the standard PSO algorithm to some extent, but the phenomenon of premature convergence in the optimization of wireless sensor network coverage still restricts the improvement of coverage. Aiming at the problem of premature convergence, this paper improves the adaptive PSO algorithm and the VFPSO algorithm on the basis of which the evolutionary degree of inertia weight of the adaptive PSO algorithm is added to the comparison of the offspring before the historical optimal mean of all particles. Make the calculation basis of evolution degree more comprehensive; Dimension selection mechanism is introduced into VFPSO algorithm, which makes the intervention of random disturbance to precocious problem more efficient. Similar to PSO algorithm, BBO algorithm (Biogeography-based Optimization,BBO) is also a kind of multi-objective optimization algorithm with strong ability of information exchange. Its improved algorithm (VF-BBO algorithm) can improve the coverage of sensor network better. Therefore, the VF-BBO algorithm is regarded as the performance comparison algorithm of two improved PSO algorithms. Through the comparison of the algorithms before and after the improvement, and combined with the VF-BBO algorithm as the reference for the optimization of coverage, the two improved PSO algorithms solve the problem of premature convergence and increase the coverage of wireless sensor networks by 5%.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.5;TP212.9
本文編號：2319767
[Abstract]:In recent years, with the rapid development of wireless sensor network (Wireless Sensor Network,WSN) technology, it has received wide attention in various industries. The coverage quality of wireless sensor network is related to the efficiency of the whole system. There are many reference standards for evaluating the coverage quality of wireless sensor networks, such as network coverage, network connectivity, network energy consumption, communication delay rate, etc. In this paper, the coverage of wireless sensor networks is taken as the index to evaluate the performance of wireless sensor networks, and the main purpose of this paper is to improve the coverage of wireless sensor networks to optimize the coverage of wireless sensor networks. Particle Swarm Optimization (Particle Swarm Optimization,PSO) is a mechanism of information exchange between Kennedy and Eberhart, which simulates the selective selection of birds in the process of foraging by observing their foraging behavior. The PSO algorithm has been widely used because of its advantages of simplicity and ease, and has been improved by a great deal of work because of its shortcomings such as premature convergence and low searching accuracy. Wireless sensor networks are generally composed of a large number of sensor nodes, so wireless sensor network coverage optimization is a multi-objective optimization problem. In the PSO algorithm, each particle of the particle swarm carries certain information, which corresponds to the potential solution of the coverage optimization problem in wireless sensor networks. Each particle has multi-dimensional properties and can be used for deploying sensor nodes. In the evolution of particles, poor particles improve their solutions by learning from better particles (that is, the deployment position of sensor nodes). After many iterations, the PSO algorithm has the characteristics of multi-particle multi-dimension and strong information exchange ability, which makes it suitable to solve the multi-objective dynamic optimization problem of wireless sensor network coverage optimization. Although the standard PSO algorithm has some information exchange ability, it has some problems such as premature convergence and poor optimization ability, so the optimization performance is not ideal in solving the coverage problem of wireless sensor networks. The following adaptive PSO algorithm and VFPSO algorithm improve the shortcomings of the standard PSO algorithm to some extent, but the phenomenon of premature convergence in the optimization of wireless sensor network coverage still restricts the improvement of coverage. Aiming at the problem of premature convergence, this paper improves the adaptive PSO algorithm and the VFPSO algorithm on the basis of which the evolutionary degree of inertia weight of the adaptive PSO algorithm is added to the comparison of the offspring before the historical optimal mean of all particles. Make the calculation basis of evolution degree more comprehensive; Dimension selection mechanism is introduced into VFPSO algorithm, which makes the intervention of random disturbance to precocious problem more efficient. Similar to PSO algorithm, BBO algorithm (Biogeography-based Optimization,BBO) is also a kind of multi-objective optimization algorithm with strong ability of information exchange. Its improved algorithm (VF-BBO algorithm) can improve the coverage of sensor network better. Therefore, the VF-BBO algorithm is regarded as the performance comparison algorithm of two improved PSO algorithms. Through the comparison of the algorithms before and after the improvement, and combined with the VF-BBO algorithm as the reference for the optimization of coverage, the two improved PSO algorithms solve the problem of premature convergence and increase the coverage of wireless sensor networks by 5%.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.5;TP212.9
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 吳曉軍;楊戰(zhàn)中;趙明;;均勻搜索粒子群算法[J];電子學(xué)報(bào);2011年06期
2 李磊;張寶賢;黃河清;劉海濤;;無線傳感器網(wǎng)絡(luò)路徑覆蓋問題研究[J];電子與信息學(xué)報(bào);2010年10期
3 張利彪,周春光,馬銘,劉小華;基于粒子群算法求解多目標(biāo)優(yōu)化問題[J];計(jì)算機(jī)研究與發(fā)展;2004年07期
4 袁浩;;基于粒子群算法的WSN路徑優(yōu)化[J];計(jì)算機(jī)工程;2010年04期
5 仲元昌;趙貞貞;王恒;宋揚(yáng);;無線傳感器網(wǎng)絡(luò)中的覆蓋優(yōu)化算法[J];計(jì)算機(jī)工程;2012年08期
6 張長勝;孫吉貴;歐陽丹彤;張永剛;;求解車間調(diào)度問題的自適應(yīng)混合粒子群算法[J];計(jì)算機(jī)學(xué)報(bào);2009年11期
7 林祝亮;馮遠(yuǎn)靜;;基于多粒子群算法的WSNs覆蓋優(yōu)化策略研究[J];計(jì)算機(jī)應(yīng)用研究;2009年12期
8 馮智博;黃宏光;李奕;;基于改進(jìn)粒子群算法的WSN覆蓋優(yōu)化策略[J];計(jì)算機(jī)應(yīng)用研究;2011年04期
9 宋明智;楊樂;;基于改進(jìn)自適應(yīng)PSO算法的WSN覆蓋優(yōu)化方法[J];計(jì)算機(jī)應(yīng)用研究;2013年11期
10 蔣杰;方力;張鶴穎;竇文華;;無線傳感器網(wǎng)絡(luò)最小連通覆蓋集問題求解算法[J];軟件學(xué)報(bào);2006年02期
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