發(fā)布時間：2018-06-02 06:38

  本文選題：粒子群算法 + 稀布陣列天線方向圖　； 參考：《成都理工大學》2017年碩士論文

【摘要】：稀布陣列天線不僅在主瓣波束,旁瓣,零陷等方向圖上有突出的優(yōu)勢,而且還能降低天線系統(tǒng)的建造成本及復雜度。研究陣列天線方向圖的綜合技術,旨在確定陣列天線的激勵參數(shù),使天線陣的某些輻射特性滿足給定的指標要求,或者使陣列的輻射方向圖盡可能地逼近所期望的方向圖。在實際工程中,可能需要低旁瓣,窄波束,或者在某一指定位置具有深零點等方向圖�；�于此,研究更為高效的粒子群算法在稀布陣方向圖上的應用,解決上述問題,具有重要的現(xiàn)實意義和應用價值。本文在深入探索和研究粒子群算法理論及特性的基礎上,圍繞粒子群算法在求解不同稀布陣列天線方向圖問題時的需求,提出了幾種改進粒子群算法的方法,并進行相關實驗及數(shù)據(jù)分析。本文主要研究內容如下:(1)本文將粒子群算法與遺傳算法相結合,提出了雜交粒子群算法。本文將遺傳算法中雜交變異的特點引入到粒子群算法中,改變粒子種群的多樣性,使粒子更容易跳出局部最優(yōu)解,尋找全局最優(yōu)解,同時也提高了搜索能力,改變了算法的性能。(2)本文將粒子群算法與模擬退火算法相結合,提出了退火粒子群算法。本文運用模擬退火算法的方法初始化粒子群,使粒子群算法初始種群能夠均勻覆蓋整個搜索空間,避免了傳統(tǒng)初始化方法在解決高維空間優(yōu)化問題時易于向邊緣聚集的現(xiàn)象,有利于粒子群算法在高維空間中的尋優(yōu)。同時將模擬退火思想引入到粒子群算法中,結合了粒子群算法的快速尋優(yōu)能力和模擬退火的概率突跳特性,使算法可以跳出局部最優(yōu)從而實現(xiàn)全局最優(yōu),達到更好的收斂精度。(3)為了使粒子群算法更有效地解決稀疏陣方向圖問題,結合混沌算法的優(yōu)點,本文提出了混沌粒子群算法。本文首先利用混沌序列初始化粒子的速度和位置,提高整個種群搜索的遍歷性。其次,根據(jù)當前整個種群搜索到的最優(yōu)位置產(chǎn)生混沌序列,將新產(chǎn)生的最優(yōu)位置代替當前種群中的一個粒子的位置。引入混沌序列的搜索算法可在進化過程中產(chǎn)生局部最優(yōu)解的許多鄰域點,以此幫助惰性粒子逃離局部極小點,并快速搜尋到最優(yōu)解,改善算法的搜索能力。(4)本文采用改進后的粒子群算法,分別求解不同的稀布陣列天線方向圖問題。首先,本文將雜交粒子群算法應用到稀布陣旁瓣方向圖中,為了驗證該算法的性能,將該算法用于兩個典型的稀布陣優(yōu)化布陣設計中,并將求解結果同粒子群算法和遺傳算法的最優(yōu)解進行比較,得到該方法的求解精度和速度都優(yōu)于粒子群算法和遺傳算法。其次,本文將退火粒子群算法應用到稀布陣零陷方向圖中,設計在某一指定位置有深零點的稀布陣,經(jīng)過算法的迭代優(yōu)化,得到比較好的陣元分布,通過與其他算法優(yōu)化過后的結果進行對比分析,體現(xiàn)了退火粒子群算法的優(yōu)點。最后,本文將混沌粒子群算法應用到稀疏陣方向圖中,運用混沌粒子群算法設計不同的稀疏直線陣,并與已有文獻結果進行比較,顯示了混沌粒子群算法在求解此類問題的有效性。
[Abstract]:The sparse array antenna has a prominent advantage not only in the direction of the main lobe, side lobe and zero sink, but also in reducing the construction cost and complexity of the antenna system. The comprehensive technique of the antenna array is studied to determine the excitation parameters of the array antenna so that some radiation characteristics of the antenna array can meet the given index requirements or make the antenna array meet the requirements of the given index. The radiation pattern of the array is as close to the desired direction as possible. In practical engineering, it may require low side lobe, narrow beam, or a deep zero point in a certain position. Based on this, it is of great practical significance to study the more efficient particle swarm optimization in the dilute array direction and solve the above problems. On the basis of the deep exploration and study of the theory and characteristics of particle swarm optimization, this paper puts forward several methods to improve particle swarm optimization, and carries out related experiments and data analysis. The main contents of this paper are as follows: (1) in this paper, the main contents of this paper are as follows: (1) the particle swarm optimization (PSO) Hybrid particle swarm optimization (PSO) is proposed in this paper. In this paper, hybrid particle swarm optimization (PSO) is proposed in this paper. In this paper, the characteristics of hybrid mutation in the genetic algorithm are introduced into particle swarm optimization (PSO), and the diversity of the particle population is changed to make the particle more easily jump out of the local optimal solution and find the global optimal solution. At the same time, the search capability is improved and the performance of the algorithm is changed. (2) (2) The particle swarm algorithm is combined with simulated annealing algorithm, and the annealing particle swarm optimization algorithm is proposed. This paper uses the simulated annealing algorithm to initialize the particle swarm, so that the initial population of the particle swarm optimization algorithm can cover the whole search space evenly, and avoids the traditional initialization method which can easily aggregate to the edge when it solves the problem of high dimensional space optimization. It is beneficial to the optimization of particle swarm optimization in high dimensional space. At the same time, the simulated annealing idea is introduced into particle swarm optimization (PSO), the fast optimization ability of particle swarm optimization and the probability jump characteristic of simulated annealing is combined, so that the algorithm can jump out of the local optimal and achieve the best global optimization. (3) in order to make the particle the particle swarm optimization, the particle swarm optimization algorithm can achieve better convergence accuracy. Group algorithm is more effective to solve the problem of sparse array pattern. Combining the advantages of chaos algorithm, this paper proposes a chaotic particle swarm optimization algorithm. Firstly, the velocity and location of particles are initialized by chaotic sequence, and the ergodicity of the whole population search is improved. Secondly, the chaotic sequence is generated according to the optimal location of the current whole species group, and the new chaotic sequence will be generated. The optimal position is replaced by the position of a particle in the current population. The search algorithm introduced into the chaotic sequence can generate a number of neighborhood points of the local optimal solution in the evolutionary process, in order to help the inert particles escape from the local minima, and quickly search for the optimal solution and improve the searching ability of the algorithm. (4) the improved particle swarm is adopted in this paper. In order to verify the performance of the algorithm, the hybrid particle swarm optimization (PSO) algorithm is applied to the sparse array sidelobe pattern. In order to verify the performance of the algorithm, the algorithm is applied to the design of two typical sparse array arrays, and the solution results are in the same way as the optimal solution of particle swarm optimization and genetic algorithm. In comparison, the accuracy and speed of the method are better than particle swarm optimization and genetic algorithm. Secondly, this paper applies the annealing particle swarm algorithm to the sparse array zero subsidence direction map, designs the sparse array with deep zero in a certain position, and through the iterative optimization of the algorithm, the better array element distribution is obtained, through which the algorithm is superior to other algorithms. The results are compared and analyzed, which embodies the advantages of the annealing particle swarm optimization. Finally, the chaotic particle swarm optimization algorithm is applied to the sparse array direction map, and the chaotic particle swarm algorithm is used to design different sparse linear arrays, and compared with the existing literature results, the chaotic particle swarm optimization algorithm is shown to solve such problems. Efficiency.
【學位授予單位】：成都理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TN820

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