本文選題：群集智能 + 粒子群優(yōu)化算法　； 參考：《遼寧工程技術(shù)大學(xué)》2014年碩士論文

【摘要】：粒子群優(yōu)化算法(PSO)是一種群集智能搜索算法,來源于對鳥類捕食的行為模擬和其模型構(gòu)建。因其定義簡單,為解決復(fù)雜優(yōu)化問題另辟蹊徑且十分有效,因此,許多學(xué)者對其十分關(guān)注,且經(jīng)過研究,該算法已在眾多領(lǐng)域得以廣泛應(yīng)用。但由于其理論還很不完善,還存在過早收斂的問題。到目前為止,為改善這些不足,許許多多的改進算法被提出。本文在此基礎(chǔ)上,以提高算法性能為最終目的,也深入研究了關(guān)于PSO的改進方法,并將此方法成功應(yīng)用于傳感器網(wǎng)絡(luò)的節(jié)點定位中。本文先是通過對粒子軌跡和算法收斂性的分析,分別對簡化PSO系統(tǒng)和一般化PSO系統(tǒng)進行研究分析。另外對無約束的軌跡實例分析,更直觀的描述了粒子的收斂性、周期性和離散性�；诖饲胺治稣页鲋率惯^早收斂的成分和對其的解決方式。經(jīng)過研究分析,得出算法的全局收斂條件和局部收斂條件。本文通過對基于慣性權(quán)重、學(xué)習(xí)因子、收縮因子、混合算法的改進PSO算法的研究,以傳感器的節(jié)點定位為研究背景,充分利用混沌映射的優(yōu)勢,并結(jié)合PSO,提出了新的改進算法,仿真研究證實,該算法確實可以優(yōu)化傳感器網(wǎng)絡(luò)的節(jié)點定位問題,并使其定位精度得以提高,定位速度得以加快,在傳感器節(jié)點定位問題上是一種確實可行的解決途徑。
[Abstract]:Particle Swarm Optimization (PSO) is a cluster intelligent search algorithm derived from the behavior simulation and modeling of bird prey. Because its definition is simple and it is very effective to solve complex optimization problems, many scholars pay close attention to it, and through research, the algorithm has been widely used in many fields. But because its theory is still very imperfect, there is still the problem of premature convergence. So far, in order to improve these deficiencies, many improved algorithms have been proposed. On the basis of this, and with the aim of improving the performance of the algorithm, this paper also deeply studies the improved method of PSO, and successfully applies this method to node localization in sensor networks. Firstly, by analyzing the particle trajectories and the convergence of the algorithm, the simplified PSO system and the generalized PSO system are studied and analyzed respectively in this paper. In addition, the convergence, periodicity and dispersion of particles are described more intuitively by the analysis of unconstrained trajectory examples. Based on the previous analysis, find out the components that lead to premature convergence and the solution to it. By studying and analyzing, the global and local convergence conditions of the algorithm are obtained. In this paper, an improved PSO algorithm based on inertial weight, learning factor, contraction factor and hybrid algorithm is studied. Based on the sensor node location, the advantage of chaotic mapping is fully utilized, and a new improved algorithm is proposed. The simulation results show that the proposed algorithm can effectively optimize the node location problem of sensor networks and improve the accuracy and speed of localization. It is a feasible solution to the problem of sensor node location.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212.9;TN929.5;TP18

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本文編號：1937212