本文關(guān)鍵詞：基于相關(guān)性分析的合作型協(xié)同進(jìn)化算法　出處：《南昌航空大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 協(xié)同進(jìn)化算法 相關(guān)性分析 合作型 構(gòu)造函數(shù) 多目標(biāo)化策略

【摘要】：協(xié)同進(jìn)化算法是一種模擬生態(tài)學(xué)中協(xié)同進(jìn)化現(xiàn)象的算法,協(xié)同的對象涉及到很多個層面,比如個體協(xié)同,種群協(xié)同,評估協(xié)同等,不同的層面會使用到不同的協(xié)同策略。合作型協(xié)同進(jìn)化算法是將復(fù)雜問題分解成子問題,子問題間再合作進(jìn)化,在這種合作機制的作用下,使得協(xié)同進(jìn)化算法在解決復(fù)雜大規(guī)模問題方面更具優(yōu)勢。但是對于合作型協(xié)同中對問題的分解,又面臨新的問題,不合適的分組會導(dǎo)致原問題維數(shù)間的相關(guān)性被破壞,進(jìn)而影響算法的性能。本文通過對優(yōu)化問題不同維數(shù)間相關(guān)性分析,得到對問題的合適分組,在此分組的指導(dǎo)下問題被分解成若干個子問題,進(jìn)而再相互合作求解問題的完整最優(yōu)解。通過對已有優(yōu)化問題相關(guān)性的測試分析,提出一種直接用于二進(jìn)制的測試問題構(gòu)造方法。針對測試問題維數(shù)間的相關(guān)性不能直接度量,本文以信息論中的概念為基礎(chǔ),借助不同基因位與函數(shù)值的聯(lián)合熵,間接的反映維數(shù)間相關(guān)性�；跇颖驹O(shè)計算子,使用新的相關(guān)性度量方法對構(gòu)造函數(shù)維度間的相關(guān)性進(jìn)行度量;使用聚類算子將具有不同相關(guān)性的維度進(jìn)行分類并分組;使用合作算子實現(xiàn)子問題間的協(xié)同。提出基于相關(guān)性的合作型協(xié)同進(jìn)化算法。本文研究的內(nèi)容與成果如下:(1)分析對比幾種常用的相關(guān)性度量方法,分別介紹用于二進(jìn)制編碼和實數(shù)編碼的相關(guān)性度量,并通過實驗測試了部分實數(shù)型優(yōu)化問題的相關(guān)性。(2)研究了信息熵對相關(guān)性的影響;針對現(xiàn)有優(yōu)化問題的種類貧乏,構(gòu)造一種可直接用于二進(jìn)制種群計算的測試函數(shù)集;使用已有的相關(guān)性度量方法對構(gòu)造函數(shù)進(jìn)行實驗測試,針對其不足本文又提出一種新的改進(jìn)度量方法,并通過實驗對比與已有方法的區(qū)別,證明了新方法的相關(guān)性度量更具有可分辨性。(3)基于新的相關(guān)性度量方法,提出基于相關(guān)性分析的合作型協(xié)同進(jìn)化算法。在樣本設(shè)計算子和聚類算子的作用下,找出進(jìn)化過程中問題的合適分組,分組后的子種群受合作算子的影響,互相共享并傳遞優(yōu)秀的基因信息。并通過實驗仿真,證明了新算法在解決復(fù)雜不完全可分問題上具有明顯的優(yōu)勢。(4)介紹多目標(biāo)化協(xié)同策略下的元胞遺傳算法,基于元胞空間構(gòu)造一種新的附加函數(shù)用于目標(biāo)函數(shù)的協(xié)同評估,并設(shè)計多目標(biāo)化的元胞演化策略,通過實驗測試其效果,得出此算法能夠在求解問題的同時兼顧多樣性,避免陷入局部最優(yōu)。
[Abstract]:Co evolutionary algorithm is a co evolutionary phenomenon in ecology simulation algorithm, the cooperative object involves many aspects, such as individual cooperative, collaborative evaluation of coordination, population, different levels will use different collaborative strategies. Cooperative co evolutionary algorithm is to decompose a complex problem into sub problems, then sub problem in the evolution of cooperation, cooperation mechanism, the co evolution algorithm in solving large-scale complex problems has more advantages. But for the decomposition of the problem of cooperative, and facing new problems, appropriate grouping will lead to the original problem between the correlation dimension is destroyed, and then influence the performance of the algorithm in this paper. Through the analysis of the correlation between the different dimension optimization problem, get the right group of the problem, under the guidance of this grouping problem is decomposed into several sub problems, and then mutual cooperation Complete its optimal solution. Based on the existing optimization problem of correlation test and analysis, put forward a direct test for problems. Aiming at the construction method of binary correlation test problems across the dimensions of direct measurement, based on the concept of information theory, with the aid of the same gene combined with entropy and the function value, reflect the dimension indirect correlations of the sample design. Using the new operator based on correlation measurement method to measure the correlation between the dimensions of the constructor; use the clustering operator with different correlation dimensions are classified and divided into groups; the use of cooperation between the sub problem operator to achieve synergy. Cooperative co evolutionary algorithm based on the correlation of the contents and results of this paper are put forward. The research is as follows: (1) measurement method of correlation analysis comparison of several commonly used, are introduced for related binary encoding and real encoding degree The amount, and test the part optimization problem through correlation experiment. (2) studied the effect of information entropy on the correlation of the existing species; optimization problem of the poor, to construct a test function can be directly used for calculation of binary population set; correlation using the existing measuring methods of test of the constructor, aiming at the lack of this paper proposes a new improved measurement method, and by the difference between the existing and experimental contrast method, a new method to prove the relevance measure has more resolution. (3) a new correlation measurement method based on Cooperative Coevolutionary Algorithm Based on correlation analysis is proposed. In the design of sample clustering operator and operator under the action, find appropriate grouping problem in the evolutionary process, after grouping sub populations affected by the cooperation of operators, mutual sharing and transfer genetic information excellent. And through the experimental simulation Really, it proves that the new algorithm in solving the complex can not be divided has obvious advantages on the issue. (4) introduce the multi-objective collaborative strategy of cellular genetic algorithm, cellular space to construct an additional function for new cooperative assessment based on objective function, cellular and design multi-objective evolutionary strategies through the experimental test, the effect of this algorithm is derived to take into account the diversity in solving the problems at the same time, to avoid falling into the local optimum.

【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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