發(fā)布時(shí)間：2018-06-12 18:37

  本文選題：粒子群優(yōu)化算法 + 細(xì)菌群體趨藥性算法��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：由于實(shí)際優(yōu)化問題情況復(fù)雜,傳統(tǒng)優(yōu)化方法對(duì)優(yōu)化問題的依賴性強(qiáng),在解決復(fù)雜、困難的優(yōu)化問題時(shí),往往具有較大的局限性;因此優(yōu)化效果好、可用性強(qiáng)的群體智能算法獲得發(fā)展,并被廣泛用于自動(dòng)化控制、模式識(shí)別、人工智能等各個(gè)領(lǐng)域。本文主要研究了群體智能算法中的粒子群優(yōu)化算法(Particle Swarm Optimization,PSO),將其與細(xì)菌群體趨藥性算法(Bacterial Colony Chemotaxis optimization,BCC)相結(jié)合,提出了一種混合粒子群算法—Particle Swarm Optimization and Bacterial Colony Chemotaxis optimization(PSOBCC),并將其應(yīng)用于陣列天線進(jìn)行降低旁瓣電平和生成深零點(diǎn)。論文的主要研究成果如下:(1)對(duì)粒子群算法的基本概念、實(shí)現(xiàn)方式、缺陷以及改進(jìn)方式進(jìn)行分析描述,進(jìn)一步闡述了該算法的研究現(xiàn)狀和發(fā)展趨勢。(2)為了提高算法搜索速度,本文改變了粒子群算法的更新公式,只保留位置項(xiàng)進(jìn)行迭代更新,并重新設(shè)置了慣性權(quán)重和學(xué)習(xí)因子的取值;同時(shí)為了提高算法的收斂精度,引入細(xì)菌群體趨藥性算法進(jìn)行局部搜索。整個(gè)優(yōu)化過程中,對(duì)全局最優(yōu)值進(jìn)行隨機(jī)擾動(dòng),并提出了精英替換策略。(3)對(duì)優(yōu)化算法的一些常用測試函數(shù)進(jìn)行研究,并將算法用單峰測試函數(shù)、多峰測試函數(shù)、經(jīng)過旋轉(zhuǎn)平移的經(jīng)典測試函數(shù)這三類測試函數(shù)分別進(jìn)行測試,并與一些最新的和經(jīng)典的算法進(jìn)行對(duì)比。(4)將本文的混合粒子群算法應(yīng)用于陣列天線的方向圖綜合中,針對(duì)陣列天線中的低旁瓣和深零點(diǎn)進(jìn)行優(yōu)化,并取得了較好的結(jié)果。
[Abstract]:Because the actual optimization problem is complex and the traditional optimization method is strongly dependent on the optimization problem, it often has great limitations in solving the complex and difficult optimization problem, so the optimization effect is good. Swarm intelligence algorithms with high availability have been developed and widely used in automation control, pattern recognition, artificial intelligence and other fields. In this paper, particle swarm optimization (PSO) algorithm in swarm intelligence algorithm is studied, which is combined with bacterial colony chemotaxis algorithm (Bacterial Colony Chemotaxis optimization BCCs). A hybrid particle swarm optimization algorithm (-Particle Swarm Optimization and Bacterial Colony Chemotaxis optimization PSOBCC) is proposed and applied to array antennas to reduce sidelobe level and generate deep zeros. The main research results of this paper are as follows: (1) analyzing and describing the basic concept, implementation, defect and improvement of PSO, and further expounding the research status and developing trend of PSO.) in order to improve the search speed of PSO, In this paper, the updating formula of particle swarm optimization algorithm is changed, only the position term is reserved for iterative updating, and the values of inertia weight and learning factor are reset, meanwhile, in order to improve the convergence accuracy of the algorithm, This paper introduces the bacterial population drug-seeking algorithm to carry on the local search. In the whole optimization process, the global optimal value is randomly perturbed, and the elite substitution strategy is proposed. Some common test functions of the optimization algorithm are studied, and the single-peak test function and the multi-peak test function are used in the algorithm. The three kinds of test functions are tested respectively after rotation and translation, and compared with some new and classical algorithms, the hybrid particle swarm optimization algorithm is applied to the pattern synthesis of array antenna. The low sidelobe and deep zero of array antenna are optimized, and good results are obtained.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TN820

【參考文獻(xiàn)】

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本文編號(hào)：2010680