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  本文選題：粒子群優(yōu)化 + 遺傳算法�。� 參考：《云南大學(xué)》2015年碩士論文

【摘要】：本文將遺傳改進的離散的粒子群優(yōu)化引入到特征選擇中,并由此構(gòu)建甲狀腺結(jié)節(jié)惡性風(fēng)險評估診療系統(tǒng)。本文主要包括以下內(nèi)容： 首先,對優(yōu)化問題、粒子群優(yōu)化、遺傳算法和特征選擇等概念作了概述。深入分析了粒子群優(yōu)化,回顧了幾種主要的粒子群優(yōu)化改進算法。最后,介紹了特征選擇的相關(guān)概念。 然后,利用離散的粒子群優(yōu)化具有天然編碼的這一特性,將遺傳算法的基本操作施用于離散的粒子群優(yōu)化中,實現(xiàn)了基于遺傳改進的離散的粒子群優(yōu)化的核心算法,并對其算法性能進行了標準函數(shù)測試。根據(jù)課題目標,將該算法應(yīng)用于特征選擇問題中,并與其他幾種主要的特征選擇方法進行了對照試驗。試驗證明,本文提出的遺傳改進的粒子群優(yōu)化能明顯地提升特征選擇的尋優(yōu)能力。 根據(jù)上述工作,構(gòu)建甲狀腺結(jié)節(jié)惡性風(fēng)險評估診療系統(tǒng)。在構(gòu)建分類器之前,運用數(shù)字圖像處理的技術(shù)對甲狀腺結(jié)節(jié)的超聲影像進行了特征提取,共提取出79個結(jié)節(jié)圖像的形態(tài)和紋理特征。隨后,將遺傳改進的粒子群優(yōu)化用于上述特征的選擇。將得到的最優(yōu)特征子集作為特征向量,用支持向量機對甲狀腺結(jié)節(jié)的良惡性進行分類識別,訓(xùn)練得到的分類器精度可達到88.20%,性能超過了常規(guī)特征選擇方法得到的同類分類器。根據(jù)研究結(jié)果可以看出,結(jié)節(jié)的緊致度、平滑度等在對甲狀腺結(jié)節(jié)進行良惡性無創(chuàng)預(yù)判中起到關(guān)鍵作用。 最后,在研究粒子群優(yōu)化、遺傳算法等進化計算算法的基礎(chǔ)上,擴展到隨機過程等相關(guān)的領(lǐng)域,并做了簡要的敘述,以此描繪了未來工作的主要方向和領(lǐng)域。
[Abstract]:In this paper, the discrete particle swarm optimization based on genetic improvement is introduced to feature selection, and a diagnosis and treatment system for malignant risk assessment of thyroid nodules is constructed. This paper mainly includes the following contents: Firstly, the concepts of optimization problem, particle swarm optimization, genetic algorithm and feature selection are summarized. In this paper, particle swarm optimization (PSO) is deeply analyzed, and several improved PSO algorithms are reviewed. Finally, the concept of feature selection is introduced. Then, taking advantage of the natural coding property of discrete particle swarm optimization, the basic operation of genetic algorithm is applied to discrete particle swarm optimization, and the core algorithm of discrete particle swarm optimization based on genetic improvement is realized. The performance of the algorithm is tested by standard function. According to the objective of this paper, the algorithm is applied to the feature selection problem and compared with other major feature selection methods. The experiments show that the improved particle swarm optimization proposed in this paper can obviously improve the ability of feature selection. Based on the above work, the diagnosis and treatment system of thyroid nodule malignant risk assessment was constructed. Before the classifier was constructed, digital image processing technique was used to extract the features of thyroid nodule images, and 79 nodule images were extracted from the features of morphology and texture. Then, the genetic improved particle swarm optimization is applied to the selection of the above characteristics. Using the optimal feature subset as the feature vector, the support vector machine is used to classify the benign and malignant thyroid nodules. The trained classifier has a precision of 88.20, and its performance is better than the similar classifier obtained by the conventional feature selection method. According to the results, the tightness and smoothness of the nodules play a key role in the prediction of benign and malignant thyroid nodules. Finally, based on the research of particle swarm optimization (PSO) and genetic algorithm (GA), this paper extends to the related fields such as stochastic processes, and gives a brief description, which describes the main directions and fields of future work.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R581;TP18

【參考文獻】

相關(guān)期刊論文 前1條

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本文編號：1931624