Because of the multimodal of the solution of the inverse problem , traditional deterministic optimization algorithm is often useless to solve this kind of problems 由于逆問題的解具有多峰值性,傳統(tǒng)的確定類優(yōu)化算法很難實現(xiàn)逆問題的求解。
Students lead most of the class sessions in 15 . 099 , using the conference - quality presentations they have created . course readings revolve around this term ' s topic , randomized methods for deterministic optimization 在15 . 099課程中,大部分的課時由學(xué)生來領(lǐng)導(dǎo),使用學(xué)生自己做的達到學(xué)術(shù)會議標(biāo)準(zhǔn)的陳述報告。課程閱讀圍繞這學(xué)期的主題:確定性最佳化的隨機方法。
In keeping with the tradition of the last twenty - some years , the readings in optimization seminar will focus on an advanced topic of interest to a portion of the mit optimization community : randomized methods for deterministic optimization 與過去20多年的傳統(tǒng)一致,最佳化書報討論課程的重點將放在麻省理工學(xué)院一部分最佳化團體感興趣的一個高級主題上:確定性最佳化的隨機方法。
Pseudo excitation method ( pem ) is used , thus one random process excitation can be transformed into a deterministic transient excitation , so the joint - random problem is turned into a single - random problem accurately , it can be solved easily by means of perturbation method and sequence orthogonal decomposition theory respectively . the probabilistic approach is used to transform stochastic optimization into deterministic optimization , therefore the optimization can be achieved through multiple objective decision making theory 以虛擬激勵法為基礎(chǔ),將隨機過程激勵轉(zhuǎn)化為確定性動力激勵,從而將復(fù)合隨機問題精確地轉(zhuǎn)化為僅結(jié)構(gòu)參數(shù)具有隨機性的問題,分別利用攝動理論和次序正交分解理論推導(dǎo)了確定性動力激勵下隨機結(jié)構(gòu)響應(yīng)特征,采用概率方法將隨機優(yōu)化問題轉(zhuǎn)化為確定性優(yōu)化問題,從而可以通過多目標(biāo)決策理論進行結(jié)構(gòu)優(yōu)化設(shè)計。
The main contributions of this dissertation are as follows : ( 1 ) a eugenic evolution strategy was proposed to improve the efficiency of the conventional simple genetic algorithm ( sga ) searching . the eugenic evolution genetic algorithm ( ega ) collects the population information along the evolution of children generations and constructs a deterministic optimization algorithm , which will be embedded in the evolution process at appropriate stage to speed up the local searching 由于優(yōu)化方法在建模中有相當(dāng)重要的作用,因此,接著對具有全局尋優(yōu)性能的遺傳算法進行了較為深入的研究,提出了基于優(yōu)生演進策略的遺傳算法( ega ) ,使尋優(yōu)性能有較大的提高,并成功應(yīng)用于化工領(lǐng)域中重油熱解模型參數(shù)的估計。