本文選題：隨機漂移粒子群算法 + 生物網(wǎng)絡(luò)　； 參考：《浙江大學》2017年碩士論文

【摘要】：作為生物學與工程學的交叉學科,合成生物學結(jié)合了系統(tǒng)辨識和控制理論諸多特征,包括反饋和振蕩行為等�？紤]到生物系統(tǒng)的復(fù)雜性,僅憑借經(jīng)驗知識不足以準確捕獲其主要動態(tài)特征并開展深入的分析研究,因此有必要構(gòu)建生物系統(tǒng)的有效數(shù)學模型,從而獲得系統(tǒng)的機理特性。當前,以粒子群優(yōu)化為代表的多種智能優(yōu)化算法在系統(tǒng)建模領(lǐng)域取得了顯著的進展,但依然存在算法易陷入局部最優(yōu)的問題。為此,本文通過對粒子群算法的拓撲結(jié)構(gòu)進行改進來改善其全局搜索能力,并開展了生物網(wǎng)絡(luò)的參數(shù)估計及結(jié)構(gòu)設(shè)計的應(yīng)用研究。本文的主要工作包括:1.針對粒子群優(yōu)化算法和隨機漂移粒子群優(yōu)化算法存在陷入局部最優(yōu)和對參數(shù)敏感的問題,結(jié)合局部拓撲結(jié)構(gòu)具有增強算法全局搜索能力的特性,提出了馮·諾依曼拓撲結(jié)構(gòu)的隨機漂移粒子群優(yōu)化算法,并通過對經(jīng)典測試函數(shù)的尋優(yōu)仿真驗證了算法的有效性。2.針對生物網(wǎng)絡(luò)參數(shù)估計時存在的非線性難題,應(yīng)用所提出的具有較強全局搜索能力的算法進行仿真求解,并與其他四種算法的結(jié)果進行了比較,結(jié)果表明所提出的改進算法能有效提高實例系統(tǒng)的參數(shù)估計性能。3.針對合成基因振蕩網(wǎng)絡(luò)的魯棒性設(shè)計問題,提出了一種改進算法的離散型優(yōu)化方法,結(jié)合兩步優(yōu)化思想,在優(yōu)化確定網(wǎng)絡(luò)結(jié)構(gòu)的基礎(chǔ)上進行了魯棒性能的優(yōu)化設(shè)計,仿真結(jié)果表明,經(jīng)過魯棒性能的優(yōu)化后,設(shè)計得到的網(wǎng)絡(luò)在結(jié)構(gòu)和參數(shù)方面的魯棒性能得到了很大的提高。4.同時考慮結(jié)構(gòu)和參數(shù)的影響時,具有同步行為的振蕩器耦合網(wǎng)絡(luò)的優(yōu)化設(shè)計成為一個混合整數(shù)優(yōu)化問題。應(yīng)用所提出的改進優(yōu)化算法進行了典型實例的仿真研究,結(jié)果表明,經(jīng)過兩步優(yōu)化設(shè)計能夠得到具有較強同步特性的耦合網(wǎng)絡(luò)。
[Abstract]:As an interdiscipline between biology and engineering, synthetic biology combines many characteristics of system identification and control theory, including feedback and oscillatory behavior. Considering the complexity of biological system, it is not enough to capture its main dynamic characteristics and carry out in-depth analysis and research by relying on the knowledge of experience. Therefore, it is necessary to construct an effective mathematical model of biological system to obtain the mechanism characteristics of the system. At present, many intelligent optimization algorithms, represented by particle swarm optimization, have made remarkable progress in the field of system modeling, but there is still the problem that the algorithm is prone to fall into local optimization. Therefore, this paper improves the global searching ability by improving the topology of particle swarm optimization, and studies the parameter estimation and structure design of biological network. The main work of this paper includes: 1. In view of the problem that particle swarm optimization and random drift particle swarm optimization are trapped in local optimum and sensitive to parameters, the local topology has the characteristics of enhancing the global search ability of the algorithm. A random drift particle swarm optimization algorithm for von Neumann topology is proposed. The effectiveness of the algorithm is verified by the optimization simulation of classical test functions. Aiming at the nonlinear problem in the estimation of biological network parameters, the proposed algorithm with strong global searching ability is used to simulate and solve the problem, and the results are compared with the results of the other four algorithms. The results show that the proposed improved algorithm can effectively improve the performance of parameter estimation. In order to solve the problem of robust design of synthetic gene oscillation network, a discrete optimization method based on improved algorithm is proposed. Based on the optimization of network structure, a robust performance optimization design is carried out based on the idea of two-step optimization. The simulation results show that the robust performance of the designed network is greatly improved in terms of structure and parameters after the robust performance optimization. Considering the influence of structure and parameters, the optimal design of oscillator coupling network with synchronous behavior becomes a mixed integer optimization problem. The simulation results show that the coupling network with strong synchronization characteristics can be obtained by the two-step optimization design.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：Q811.4;TP18

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本文編號：1893613