本文選題：智能優(yōu)化 + 人工魚群算法��； 參考：《北京化工大學(xué)》2015年碩士論文

【摘要】：化工過程存在很多待優(yōu)化問題,但是往往都比較復(fù)雜。并且隨著化工過程規(guī)模的日益擴大,目標(biāo)函數(shù)變得愈加復(fù)雜,同時自變量和約束條件的數(shù)目也更多,利用以往的優(yōu)化算法很難解決。人工魚群算法(AFSA)是智能計算研究領(lǐng)域的一個新方向,不僅為復(fù)雜系統(tǒng)優(yōu)化問題的解決提供了一種新理論,而且給化工過程優(yōu)化問題的解決提供了一種新思路。圍繞AFSA,本文主要開展了以下工作：(1)簡述了化工優(yōu)化過程的發(fā)展歷程和所面臨的巨大挑戰(zhàn),最優(yōu)化問題的概念、分類,優(yōu)化算法的發(fā)展概況以及目前常見的幾種智能優(yōu)化算法等。(2)介紹了基本AFSA,包括算法的提出背景,人工魚的結(jié)構(gòu),及其基本行為描述、行為選擇和算法的尋優(yōu)原理等。同時,對AFSA的研究概況做了一些綜述。(3)通過若干個實驗,分析了基本AFSA當(dāng)中各個參數(shù)對算法的影響。針對AFSA的不足之處,對其進行分析改進,提出了一種可以自動地獲取虛擬人工魚視覺感知范圍與前進步長的改進人工魚群算法(IAFSA)。再利用經(jīng)典函數(shù)測試改進的人工魚群算法,證明其有效性和實用性。(4)把改進后的人工魚群算法應(yīng)用于化工過程優(yōu)化當(dāng)中。對化工生產(chǎn)中的換熱管網(wǎng)(HEN)優(yōu)化問題與丁烯烷化(BA)過程優(yōu)化問題進行了模型簡化,并且建立它們相應(yīng)的數(shù)學(xué)模型。成功的對換熱管網(wǎng)優(yōu)化問題和丁烯烷化過程優(yōu)化問題進行了優(yōu)化,提高了效益。
[Abstract]:There are many optimization problems in chemical process, but they are often complicated.With the increasing scale of chemical process, the objective function becomes more and more complex, and the number of independent variables and constraints is more, so it is difficult to solve the problem by using the previous optimization algorithm.Artificial Fish Swarm algorithm (AFSA) is a new direction in the field of intelligent computing. It not only provides a new theory for solving the optimization problems of complex systems, but also provides a new way to solve the optimization problems of chemical processes.In this paper, the following work is mainly carried out around AFSA: 1) briefly describing the development of chemical optimization process and the great challenges it faces, the concept and classification of optimization problems,This paper introduces the basic AFSAs, including the background of the proposed algorithm, the structure of the artificial fish, the basic behavior description, the behavior selection and the optimization principle of the algorithm.At the same time, a review of the research of AFSA is given. (3) through several experiments, the influence of each parameter in the basic AFSA on the algorithm is analyzed.Aiming at the shortcomings of AFSA, this paper analyzes and improves it, and proposes an improved artificial fish swarm algorithm which can automatically acquire the visual perception range and advance time of virtual artificial fish.Then the improved artificial fish swarm algorithm is tested by classical function, which proves its validity and practicability. 4) the improved artificial fish swarm algorithm is applied to the optimization of chemical process.In this paper, the heat transfer pipe network optimization problem and the butene alkylation process optimization problem in chemical production are simplified and their corresponding mathematical models are established.The optimization problem of heat transfer pipe network and alkylation process of butene was successfully optimized and the benefit was improved.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ015.9;TP18

【參考文獻】

相關(guān)期刊論文 前7條

1 施文俊,何小榮,陳丙珍,邱彤;TS法的改進及其在求解化工優(yōu)化問題中的應(yīng)用[J];化工學(xué)報;2004年10期

2 賀益君,陳德釗;連續(xù)約束蟻群優(yōu)化算法的構(gòu)建及其在丁烯烷化過程中的應(yīng)用[J];化工學(xué)報;2005年09期

3 劉耀年;范為;韓立國;;基于改進AFSA算法的電力系統(tǒng)無功優(yōu)化[J];繼電器;2008年08期

4 馮靜;舒寧;;群智能理論及應(yīng)用研究[J];計算機工程與應(yīng)用;2006年17期

5 席裕庚，柴天佑，惲為民;遺傳算法綜述[J];控制理論與應(yīng)用;1996年06期

6 張紀(jì)會,徐心和;一種新的進化算法——蟻群算法[J];系統(tǒng)工程理論與實踐;1999年03期

7 楚曉麗;朱英;石俊濤;;基于改進人工魚群算法的圖像邊緣檢測[J];計算機系統(tǒng)應(yīng)用;2010年08期

相關(guān)博士學(xué)位論文 前1條

1 李曉磊;一種新型的智能優(yōu)化方法-人工魚群算法[D];浙江大學(xué);2003年

，

本文編號：1732954