本文選題：布谷鳥算法 + 遺傳因子��； 參考：《河北大學》2016年碩士論文

【摘要】：圖像配準可以歸結為尋求最佳空間變換的多參數優(yōu)化問題,圖像融合中加權系數等參數經過優(yōu)化后會使融合效果更佳�？焖�、精確、適應性強的優(yōu)化算法是實現參數優(yōu)化的重要步驟。標準布谷鳥算法是基于布谷鳥尋窩產卵行為提出的新型智能算法,具有簡單高效、隨機路徑優(yōu)、參數少、程序運行簡單等特征,但也存在局部搜索能力相對較弱、后期搜索速度慢、計算精度不高等缺點。將遺傳因子融入標準布谷鳥算法中,提出一種基于遺傳因子的布谷鳥算法,通過選擇操作和添加交叉、變異因子,增加種群的多樣性,提高算法全局搜索能力。實驗表明,改進后算法的尋優(yōu)精度比標準布谷鳥算法高,表現出更好的收斂性和穩(wěn)定性。將混沌搜索融入標準布谷鳥算法,提出一種基于混沌搜索的布谷鳥算法,利用混沌運動隨機性、遍歷性的特點,使種群均勻分布,并增強算法跳出局部極值的能力。實驗表明,改進后的算法在搜索空間小時搜索能力增強,但其收斂性和穩(wěn)定性不如基于遺傳因子的布谷鳥算法好。為了進一步提高算法的收斂速度和計算精度,將算法中的固定參數改為隨迭代過程自適應變化的動態(tài)參數,在基于遺傳因子布谷鳥算法的基礎上提出基于遺傳因子自適應布谷鳥算法。實驗表明,基于遺傳因子自適應布谷鳥算法的收斂速度和全局尋優(yōu)能力進一步提高并且可靠性更高。最后將基于遺傳因子自適應布谷鳥算法用于圖像配準和圖像融合的參數優(yōu)化中,并與其他智能算法對比,實驗表明,該算法配準精度高、時間短,并且尋找的最優(yōu)權值系數融合后得到的融合圖像能提取更多有用信息,融合效果更佳,充分驗證了本文算法的有效性、穩(wěn)定性和可行性。
[Abstract]:Image registration can be attributed to the multi-parameter optimization problem of seeking the best spatial transformation. After the optimization of the parameters such as weighting coefficient in image fusion, the fusion effect will be better. Fast, accurate and adaptable optimization algorithm is an important step to realize parameter optimization. Standard Cuckoo algorithm is a new intelligent algorithm based on cuckoo nest and spawning behavior. It has the characteristics of simple and efficient, random path optimization, few parameters, simple program operation, etc. However, the local search ability is relatively weak. Late search speed is slow, calculation accuracy is not high shortcomings. The genetic factor is incorporated into the standard cuckoo algorithm, and a genetic factor-based cuckoo algorithm is proposed. By selecting the operation and adding the crossover and mutation factor, the diversity of the population and the global search ability of the algorithm are improved. Experimental results show that the improved algorithm has better convergence and stability than the standard Cuckoo algorithm. The chaotic search is incorporated into the standard cuckoo algorithm, and a chaotic search based cuckoo algorithm is proposed. The chaotic motion randomness and ergodicity are used to make the population distribute evenly, and the ability of the algorithm to jump out of the local extremum is enhanced. The experimental results show that the improved algorithm has a better ability to search in hours of searching space, but its convergence and stability are not as good as the genetic factor-based cuckoo algorithm. In order to further improve the convergence speed and accuracy of the algorithm, the fixed parameters in the algorithm are changed to the dynamic parameters that change adaptively with the iterative process. On the basis of genetic factor-based cuckoo algorithm, an adaptive genetic factor-based cuckoo algorithm is proposed. Experiments show that the convergence speed and global optimization ability of the adaptive cuckoo algorithm based on genetic factor are further improved and the reliability is higher. Finally, the genetic factor-based adaptive cuckoo algorithm is applied to the parameter optimization of image registration and image fusion, and compared with other intelligent algorithms, the experimental results show that the algorithm has high registration accuracy and short time. And the fusion image obtained by the fusion of the optimal weight coefficients can extract more useful information and the fusion effect is better, which fully verifies the effectiveness, stability and feasibility of the proposed algorithm.
【學位授予單位】：河北大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP18;TP391.41
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本文編號：2071113