本文關(guān)鍵詞：基于膜系統(tǒng)的粒子群優(yōu)化算法在產(chǎn)業(yè)集群演化中的研究與應(yīng)用　出處：《山東師范大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 粒子群優(yōu)化 膜系統(tǒng) 產(chǎn)業(yè)集群

【摘要】：產(chǎn)業(yè)集群，指的是某一類(lèi)相關(guān)的企業(yè)大量聚集在某一特定區(qū)域的經(jīng)濟(jì)現(xiàn)象。對(duì)于集群內(nèi)的企業(yè)來(lái)講，通過(guò)企業(yè)的聚集獲得了巨大的競(jìng)爭(zhēng)優(yōu)勢(shì)，從而取得更好的發(fā)展與豐厚的經(jīng)濟(jì)效益。 產(chǎn)業(yè)集群是一種基于自組織結(jié)構(gòu)的經(jīng)濟(jì)現(xiàn)象。自組織結(jié)構(gòu)的特征是自我適應(yīng)和自我組織，這一點(diǎn)和產(chǎn)業(yè)集群不謀而合。同樣，若將集群看作一個(gè)由眾多企業(yè)和機(jī)構(gòu)構(gòu)成的系統(tǒng)，，那它也是一種自組織系統(tǒng)。產(chǎn)業(yè)集群形成過(guò)程中也是經(jīng)由開(kāi)放的耗散結(jié)構(gòu)不斷演化而來(lái)的。 微粒群的尋優(yōu)與產(chǎn)業(yè)集群的集聚具有相通性。產(chǎn)業(yè)集群的形成實(shí)際上是一個(gè)自組織的過(guò)程，粒子群優(yōu)化算法是自組織算法，其尋優(yōu)過(guò)程也是自組織的。若將集群中的企業(yè)視為粒子群優(yōu)化算法中的粒子，集群所處的位置正是集群競(jìng)爭(zhēng)力最大的位置，將其看作粒子群優(yōu)化算法中的最優(yōu)解的位置，那么產(chǎn)業(yè)集群的聚集過(guò)程可以視為粒子群的尋優(yōu)過(guò)程。可見(jiàn)，微粒群的尋優(yōu)與產(chǎn)業(yè)集群的集聚是相通的。 基于PSO算法局限性的思考，并受到P系統(tǒng)的啟發(fā)，本文提出了一種基于P系統(tǒng)的粒子群優(yōu)化算法（P-PSO）。在本文中P系統(tǒng)中的膜有主膜和輔助膜之分，設(shè)立主膜一個(gè)，若干輔助膜。粒子被放入P系統(tǒng)之后，主膜內(nèi)粒子與輔助膜內(nèi)粒子進(jìn)行合理分工，主膜內(nèi)粒子負(fù)責(zé)“開(kāi)發(fā)”（即在輔助膜內(nèi)粒子的引導(dǎo)下，搜尋最優(yōu)解的具體位置），“探索”任務(wù)是由輔助膜內(nèi)的粒子來(lái)完成（即盡可能的遍歷搜索空間，搜尋可能存在最優(yōu)解的區(qū)域，為主膜內(nèi)粒子的搜索提供引導(dǎo)）。其中，主膜內(nèi)粒子與輔助膜粒子之間的信息交流由P系統(tǒng)中的交流規(guī)則來(lái)實(shí)現(xiàn)。為了達(dá)到探索與開(kāi)發(fā)的目的，輔助膜內(nèi)粒子需要保持較高的粒子活性，主膜內(nèi)粒子要有精細(xì)化搜索的能力。對(duì)于新算法，我們借助常用的測(cè)試函數(shù)進(jìn)行了檢測(cè)，結(jié)果表明P-PSO算法具有很好性能。 為了用粒子群優(yōu)化算法來(lái)模擬產(chǎn)業(yè)集群的形成問(wèn)題，我們將產(chǎn)業(yè)集群微粒群化。產(chǎn)業(yè)集群的競(jìng)爭(zhēng)力值為粒子群優(yōu)化算法中所求解的目標(biāo)函數(shù)的值；產(chǎn)業(yè)集群的地理坐標(biāo)為PSO算法中粒子搜索空間中的位置；集群內(nèi)部企業(yè)之間肯定有“合作”與“競(jìng)爭(zhēng)”，這可以通過(guò)PSO算法中“自我認(rèn)知”部分和“社會(huì)”部分來(lái)實(shí)現(xiàn)。 最后我們以山東汽車(chē)產(chǎn)業(yè)集群為例，運(yùn)用P-PSO算法模擬集群內(nèi)企業(yè)的聚集過(guò)程，通過(guò)實(shí)證分析，對(duì)汽車(chē)產(chǎn)業(yè)集群的發(fā)展進(jìn)行了預(yù)測(cè)。
[Abstract]:Industrial cluster refers to the economic phenomenon that a certain kind of related enterprises gather in a certain specific area in large quantities. For the enterprises in the cluster, they obtain a huge competitive advantage through the agglomeration of enterprises. In order to achieve better development and rich economic benefits. Industrial cluster is an economic phenomenon based on self-organization structure. Self-organization structure is characterized by self-adaptation and self-organization, which coincides with industrial cluster. If a cluster is regarded as a system composed of many enterprises and institutions, it is also a self-organizing system. The formation of industrial cluster is actually a process of self-organization, particle swarm optimization algorithm is self-organization algorithm. If the enterprises in the cluster are regarded as particles in the particle swarm optimization algorithm, the location of the cluster is the most competitive position. Considering it as the location of the optimal solution in PSO, the aggregation process of industrial cluster can be regarded as the optimization process of PSO, which shows that the optimization of PSO is related to the agglomeration of industrial cluster. Based on the limitation of PSO algorithm, and inspired by P system. In this paper, a particle swarm optimization algorithm based on P system is proposed. In this paper, the membrane of P system is divided into main membrane and auxiliary membrane, and a main membrane is set up. After the particles were put into the P system, the particles in the main film and the particles in the auxiliary film were divided reasonably, and the particles in the main film were responsible for the "development" (that is, under the guidance of the particles in the auxiliary film). Searching for the specific location of the optimal solution, the "exploration" task is accomplished by the particles in the auxiliary film (that is, traversing the search space as much as possible, searching for the region where the optimal solution may exist. In order to achieve the purpose of exploration and development, the information exchange between the main film particles and the auxiliary membrane particles is realized by the communication rules in P system. The particle in the auxiliary film needs to keep high particle activity, and the particle in the main film should have the ability of fine searching. For the new algorithm, we use the commonly used test function to detect the new algorithm. The results show that the P-PSO algorithm has good performance. In order to simulate the formation of industrial clusters with particle swarm optimization (PSO) algorithm, we transform the PSO into industrial clusters. The competitiveness of industrial clusters is the value of the objective function solved by PSO. The geographical coordinate of industrial cluster is the position of particle search space in PSO algorithm. There must be "cooperation" and "competition" among enterprises in the cluster, which can be realized through the part of "self-cognition" and "society" in PSO algorithm. Finally, taking Shandong automobile industry cluster as an example, we use P-PSO algorithm to simulate the clustering process of enterprises in the cluster, and predict the development of automobile industry cluster through empirical analysis.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP18

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