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1、-內(nèi)蒙古科技大學(xué)碩士學(xué)位論文5.2甲醇合成模型的建立···························································································525.2.1甲醇合成數(shù)據(jù)選取·····················································································525.2.2煤制甲醇數(shù)學(xué)模型建立·························································
2、····················535.3算法應(yīng)用················································································································585.4本章小結(jié)················································································································60結(jié)論·························
3、·········································································································62參考文獻(xiàn)······························································································································63附錄A多目標(biāo)粒子群在MATLAB中程序·································
4、····································68在學(xué)研究成果······················································································································77致謝·····························································································································
5、·····78----3----內(nèi)蒙古科技大學(xué)碩士學(xué)位論文1緒論5.2多目標(biāo)優(yōu)化問題研究背景及現(xiàn)狀優(yōu)化問題意思是在滿足指定條件下，盡可能的找出一種方法、策略亦或是一類參數(shù)值，使得所要求解的目標(biāo)問題達(dá)到最優(yōu)最好，所研究的內(nèi)容是在眾多方案中如何找到最佳的解決方案的思考方法。優(yōu)化問題已經(jīng)被大范圍地應(yīng)用在國防、軍事、鋼鐵、交通、建筑、材料、通信等許多領(lǐng)域范圍。對(duì)于優(yōu)化方法，這是一類以數(shù)學(xué)為根基，目的用來解決各類多目標(biāo)優(yōu)化問題的實(shí)用科技。許多國家科技專家都非常重視并開始研究優(yōu)化問題的理論和技術(shù)，通過各種研究創(chuàng)新，目前應(yīng)用優(yōu)化方法的各種領(lǐng)域，已經(jīng)正在或者開始產(chǎn)生了巨大的經(jīng)濟(jì)利益和社會(huì)價(jià)值
6、。經(jīng)過多次實(shí)踐驗(yàn)證，經(jīng)過優(yōu)化方法處理的工藝技術(shù)，能夠有效的提高工作效率、降低能源消耗、資源能夠合理利用，隨著優(yōu)化方法的進(jìn)一步研究及其生產(chǎn)規(guī)模的進(jìn)一步擴(kuò)大，這種合理歸置工藝技術(shù)的效果也會(huì)更加明顯。在管理學(xué)、金融學(xué)、、交通運(yùn)輸、計(jì)算機(jī)應(yīng)用科學(xué)、國防建設(shè)等學(xué)科及其諸多應(yīng)用優(yōu)化方法的領(lǐng)域中，經(jīng)常出現(xiàn)許多復(fù)雜的、難以單方面解決的多目標(biāo)優(yōu)化問題。面對(duì)這些復(fù)雜的、困難的優(yōu)化問題，盡管許多傳統(tǒng)的優(yōu)化方法，諸如牛頓尋優(yōu)法、共扼梯度法和單純形法等都能夠搜索整個(gè)尋優(yōu)空間，但是這些傳統(tǒng)的方法無法在復(fù)雜條件下完成搜索，極度輕易就會(huì)發(fā)生搜索過程中的組合爆炸?？紤]到工作問題中的許多現(xiàn)實(shí)特點(diǎn)，例如諸多條件約
7、束、函數(shù)方程的非線性、現(xiàn)實(shí)中的復(fù)雜性以及生產(chǎn)工藝難以建模等困難，要解決上述困難，優(yōu)化算法迫切呼吁尋求更加高效的尋優(yōu)方法，這成為相關(guān)學(xué)科的主要研究內(nèi)容之一。群智能優(yōu)化算法(SwarmIntelligenceOptimization)是一種典型的人工智能算法，該方法是通過對(duì)自然界中生物的群體遷徙、群體覓食等一系列大規(guī)模生物活動(dòng)，生物學(xué)家通過其表現(xiàn)出來的社會(huì)行為進(jìn)行觀察及研究發(fā)現(xiàn)，群居式的生物群體可以被當(dāng)作一個(gè)整體式系統(tǒng)，該系統(tǒng)組織嚴(yán)謹(jǐn)，活動(dòng)規(guī)律，一般呈現(xiàn)出整體規(guī)模的有規(guī)律活動(dòng)，盡管在整個(gè)系統(tǒng)中的個(gè)體都非常簡
