發(fā)布時間：2018-05-16 12:49

  本文選題：智能優(yōu)化算法 + 灰狼優(yōu)化算法�。� 參考：《河南師范大學(xué)》2017年碩士論文

【摘要】：灰狼優(yōu)化(Grey Wolf Optimization,GWO)算法是一種新穎的元啟發(fā)式智能優(yōu)化算法,其模擬了大自然中灰狼種族特有的等級制度和集體狩獵行為。GWO算法由于結(jié)構(gòu)簡單、參數(shù)少,收斂速度快等優(yōu)點在實際工程優(yōu)化問題中得到廣泛應(yīng)用,但由于該算法提出時間較晚,其理論基礎(chǔ)和算法應(yīng)用方面的研究都不完善,算法本身也存在著諸多不足,如面對復(fù)雜優(yōu)化問題時存在求解精度低、易早熟收斂等缺陷。為有效提高GWO算法的性能,拓展算法應(yīng)用領(lǐng)域,本文通過對其算法理論和進化方式進行研究和分析,提出了兩種改進方案,并將改進算法簡單應(yīng)用于多閾值圖像分割問題,主要研究內(nèi)容如下:(1)詳細描述了GWO算法的思想來源,算法原理以及實施步驟,分析討論了GWO算法的優(yōu)缺點,并歸納了目前國內(nèi)外對于GWO算法的各種改進思路,同時對GWO算法的應(yīng)用領(lǐng)域進行了總結(jié)。(2)基于等級制度對于狼群狩獵影響的深入分析,提出了一種強化狼群等級制度的灰狼優(yōu)化算法。該算法中的灰狼個體具有兩種狩獵模式:一種是跟隨狩獵模式,一種是自主探索模式。這兩種狩獵模式既能體現(xiàn)高等級灰狼對低等級灰狼的引領(lǐng)作用,又能在充分挖掘種群位置信息的基礎(chǔ)上發(fā)揮個體的自主能動性,提高種群的多樣性避免算法陷入局部極值。仿真結(jié)果表明:該算法具有更強的全局勘探能力和更高的尋優(yōu)精度。(3)針對灰狼優(yōu)化算法和差分進化算法各自在應(yīng)用上的優(yōu)勢和不足,提出了一種灰狼優(yōu)化和差分進化的混合算法,實現(xiàn)了算法之間的優(yōu)勢互補,獲得了一種全局搜索能力和局部搜索能力兼顧的高效混合優(yōu)化算法,并將混合算法用于解決復(fù)雜高維函數(shù)優(yōu)化問題,實驗分析表明,該混合算法具有更好的收斂速度和優(yōu)化性能,更適用于求解各種函數(shù)優(yōu)化問題。(4)基于對上述混合算法特性的分析,將其應(yīng)用于解決最大熵多閾值圖像分割法中存在的閾值選擇不準(zhǔn)確、分割速度慢等問題,提出了一種新型多閾值圖像分割算法。實驗結(jié)果表明,該方法能夠快速、準(zhǔn)確的找到圖像分割的最優(yōu)閾值組合,進行有效分割。
[Abstract]:Grey Wolf Optimization (GWO) algorithm is a novel meta-heuristic intelligent optimization algorithm. It simulates the class system and collective hunting behavior of the gray wolf race in nature because of its simple structure and few parameters. The advantages of fast convergence rate have been widely used in practical engineering optimization problems. However, due to the late development of the algorithm, the theoretical basis and the application of the algorithm are not perfect, and the algorithm itself has many shortcomings. For example, in the face of complex optimization problems, there are some defects such as low accuracy and premature convergence. In order to effectively improve the performance of GWO algorithm and expand its application field, through the research and analysis of its algorithm theory and evolution mode, this paper puts forward two improved schemes, and applies the improved algorithm to multi-threshold image segmentation problem. The main research contents are as follows: (1) this paper describes in detail the origin, principle and implementation steps of the GWO algorithm, analyzes and discusses the advantages and disadvantages of the GWO algorithm, and summarizes various improvements to the GWO algorithm at home and abroad. At the same time, the application field of GWO algorithm is summarized. (2) based on the deep analysis of the effect of rank system on the hunting of wolves, an optimization algorithm is proposed to strengthen the hierarchical system of wolves. The gray wolf individuals in this algorithm have two hunting modes: one is following hunting mode and the other is self-exploring mode. These two hunting modes can not only reflect the leading role of the high-grade gray wolf to the low-grade gray wolf, but also give full play to the autonomous initiative of the individual on the basis of fully excavating the information of the population location, and improve the diversity of the population to avoid the algorithm falling into the local extremum. The simulation results show that the algorithm has stronger global exploration ability and higher searching accuracy.) aiming at the advantages and disadvantages of the grey wolf optimization algorithm and differential evolution algorithm in application, A hybrid algorithm of gray wolf optimization and differential evolution is proposed, which realizes the complementary advantages between the algorithms, and obtains an efficient hybrid optimization algorithm with both global and local search capabilities. The hybrid algorithm is used to solve the complex high-dimensional function optimization problem. The experimental results show that the hybrid algorithm has better convergence speed and optimization performance. It is more suitable for solving various function optimization problems. Based on the analysis of the characteristics of the hybrid algorithm mentioned above, it is applied to solve the problems of inaccurate threshold selection and slow segmentation speed in the maximum entropy multi-threshold image segmentation method. A new multi-threshold image segmentation algorithm is proposed. The experimental results show that this method can find the optimal threshold combination of image segmentation quickly and accurately, and can effectively segment the image.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP391.41

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