發(fā)布時間：2018-07-20 11:43

【摘要】：復(fù)雜科學(xué)與工程中問題的計算量極大,常用的確定性最優(yōu)化算法面對上述問題在有限的時間內(nèi)經(jīng)常會出現(xiàn)失效的情況。因此基于自然進化過程的進化計算得到了廣泛的研究和關(guān)注,并在很多實際問題中得到了成功的應(yīng)用。然而單一的進化算法或者群集智能算法或者本身都存在某些不足之處,需要進一步改善。多元混合算法是一類基于多種單一算法相互融合共同完成優(yōu)化過程的算法,其優(yōu)點包括平衡性好,組合靈活,魯棒性強,適合復(fù)雜的優(yōu)化問題。研究者們對許多混合算法進行了研究,取得了較好的效果。然而進化計算方法眾多,相關(guān)理論和實踐并未完善,值得進一步研究。本文根據(jù)常用進化算法和群集智能算法的特點,提出了一類新的算法混合模式,串行混合、并行混合和串并行混合,選取了6種常見的優(yōu)化算法進行混合,并應(yīng)用于圖像處理優(yōu)化求解問題中,主要工作如下:1.使用CEC2015中的15個測試函數(shù)對遺傳算法、差分進化算法、粒子群算法、人工蜂群算法、杜鵑搜索算法、螢火蟲算法這六個優(yōu)化算法進行了測試,并結(jié)合算法的流程,歸納出算法的收斂性、搜索能力以及跳出局部最優(yōu)等特性。2.提出了具體的混合策略:串行混合、并行混合和串并行混合。實現(xiàn)了六種串行混合算法和六種并行混合算法,并進行了仿真測試。結(jié)果表明混合算法的性能更加均衡,在優(yōu)化復(fù)雜問題上有較好的效果。3.研究和實現(xiàn)了多元混合算法在圖像分割,圖像增強和圖像匹配中的應(yīng)用。選取了四個性能較好的混合算法以及對應(yīng)的單一算法進行對比試驗。選取的四個混合算法包括:串行粒子群杜鵑搜索算法、串行差分進化杜鵑搜索算法、并行差分進化遺傳算法、并行粒子群差分進化算法;對應(yīng)的單一算法包括:粒子群算法、杜鵑搜索算法、差分進化算法、遺傳算法。試驗結(jié)果表明混合優(yōu)化算法在圖像分割、圖像增強以及圖像匹配中能夠快速得到較優(yōu)的結(jié)果,且各算法在不同的圖像上的處理效果相對穩(wěn)定。總體來說,本文提出了一類最優(yōu)化算法的混合模式,并根據(jù)該模式實現(xiàn)了數(shù)種混合算法且在圖像分割、圖像增強、圖像匹配中進行了應(yīng)用。實驗結(jié)果表明使用該混合模式得到的混合算法繼承了對應(yīng)的單一算法的特性,對不同的優(yōu)化問題有良好的優(yōu)化性能和穩(wěn)定性。
[Abstract]:The computational complexity of the problems in complex science and engineering is very large. The commonly used deterministic optimization algorithms often fail in the limited time in the face of these problems. Therefore, evolutionary computing based on natural evolutionary processes has been widely studied and paid attention to, and has been successfully applied in many practical problems. However, the single evolutionary algorithm or cluster intelligence algorithm or itself has some shortcomings, which need to be further improved. Multivariate hybrid algorithm is a kind of algorithm based on the fusion of a variety of single algorithms to complete the optimization process together. Its advantages include good balance, flexible combination, strong robustness, suitable for complex optimization problems. Researchers have studied many hybrid algorithms and achieved good results. However, there are many evolutionary computing methods, and the relevant theory and practice are not perfect, which is worthy of further study. According to the characteristics of common evolutionary algorithms and swarm intelligence algorithms, this paper presents a new class of hybrid algorithms, serial hybrid, parallel hybrid and series-parallel hybrid, and six common optimization algorithms are selected to mix. And applied to the image processing optimization problem, the main work is as follows: 1. This paper uses 15 test functions in CEC2015 to test the genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, rhododendron search algorithm and firefly algorithm. The convergence, searching ability and jumping out of local optimum of the algorithm are summarized. 2. 2. Specific mixing strategies are proposed: serial mixing, parallel mixing and series-parallel mixing. Six serial hybrid algorithms and six parallel hybrid algorithms are implemented and simulated. The results show that the performance of the hybrid algorithm is more balanced and has a better effect on the optimization of complex problems. The application of multivariate hybrid algorithm in image segmentation, image enhancement and image matching is studied and implemented. Four hybrid algorithms with good performance and the corresponding single algorithm are selected for comparative test. The four hybrid algorithms selected include: serial particle swarm cuckoo search algorithm, serial differential evolution rhododendron search algorithm, parallel differential evolution genetic algorithm, parallel particle swarm differential evolution algorithm; Rhododendron search algorithm, differential evolution algorithm, genetic algorithm. The experimental results show that the hybrid optimization algorithm can quickly obtain better results in image segmentation, image enhancement and image matching, and the processing effect of each algorithm on different images is relatively stable. In general, this paper proposes a kind of hybrid pattern of optimization algorithm, and implements several hybrid algorithms according to this pattern, and it is applied in image segmentation, image enhancement and image matching. The experimental results show that the hybrid algorithm inherits the characteristics of the corresponding single algorithm and has good optimization performance and stability for different optimization problems.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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