本文關(guān)鍵詞： 磷蝦群優(yōu)化算法 粒子群優(yōu)化算法 聚集度 聚類(lèi)分析　出處：《北方民族大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：聚類(lèi)分析法是統(tǒng)計(jì)學(xué)中的一種重要分析技術(shù),群智能算法由于其高效的優(yōu)化處理能力而越來(lái)越受到人們的重視.本文介紹了聚類(lèi)分析以及群優(yōu)化算法的相關(guān)知識(shí),對(duì)標(biāo)準(zhǔn)磷蝦群優(yōu)化和粒子群優(yōu)化算法進(jìn)行了分析和研究,提出了基于聚集度改進(jìn)的異化磷蝦群算法(CSKH)和基于聚集度的改進(jìn)粒子群優(yōu)化算法(CSPSO).最后將這兩種改進(jìn)算法整合到聚類(lèi)分析算法中,并通過(guò)真實(shí)數(shù)據(jù)集測(cè)試了整合算法的性能.具體內(nèi)容如下:首先,針對(duì)標(biāo)準(zhǔn)磷蝦群算法存在著不易跳出局部尋優(yōu)、搜索精度低等問(wèn)題,提出了一種基于聚集度的異化磷蝦群算法.本算法根據(jù)種群多樣性指標(biāo)聚集度的變化,通過(guò)在兩個(gè)相反位置移動(dòng)方向的選擇策略來(lái)增加磷蝦進(jìn)化多樣性,同時(shí)引入了隨機(jī)數(shù)策略來(lái)模擬磷蝦的外部擾動(dòng),從而取代原磷蝦群算法中的隨機(jī)擴(kuò)散運(yùn)動(dòng)的影響.算法還引入平均距離指標(biāo)來(lái)增加局部搜索的變異概率,同時(shí)將背向最優(yōu)位置的速度方向作為搜索變異方向,從而擴(kuò)大了群體的搜索空間,保證算法的全局搜索能力.其次,針對(duì)粒子群算法極易出現(xiàn)早熟收斂的問(wèn)題,在重新定義相似度的基礎(chǔ)上構(gòu)建了聚集度概念,據(jù)此來(lái)描述種群的多樣性程度,并通過(guò)設(shè)定粒子群自適應(yīng)閾值的變化來(lái)調(diào)整粒子搜索空間,同時(shí)根據(jù)聚集度的大小對(duì)粒子重新賦值,增加種群的多樣性,從而使得算法更易于跳出局部最優(yōu).最后,將上述兩種改進(jìn)算法整合到聚類(lèi)方法之中,通過(guò)改進(jìn)算法的收斂能力來(lái)指引聚類(lèi)的進(jìn)行方向,并通過(guò)對(duì)兩組真實(shí)數(shù)據(jù)集的檢測(cè)驗(yàn)證了整合算法的性能.
[Abstract]:Cluster analysis is an important technique in statistics. Swarm intelligence algorithm has attracted more and more attention because of its high efficiency. In this paper, clustering analysis and knowledge of swarm optimization algorithm are introduced. The standard krill swarm optimization and particle swarm optimization algorithm were analyzed and studied. In this paper, an improved clustering algorithm named CSKH and an improved particle swarm optimization algorithm based on aggregation are proposed. Finally, these two improved algorithms are integrated into the clustering analysis algorithm. The performance of the integration algorithm is tested by real data set. The main contents are as follows: first of all, the standard krill swarm algorithm is difficult to jump out of the local optimization, the search accuracy is low and so on. An algorithm based on aggregation degree is proposed to increase the evolutionary diversity of krill, which is based on the change of population diversity index and the selection of moving direction in two opposite positions. At the same time, the random number strategy is introduced to simulate the external disturbance of krill, so as to replace the random diffusion motion in the original krill swarm algorithm. The average distance index is also introduced to increase the mutation probability of local search. At the same time, the velocity direction of the optimal position is taken as the direction of search mutation, which expands the search space of the population and ensures the global search ability of the algorithm. Secondly, the particle swarm optimization algorithm is prone to premature convergence. Based on the redefinition of similarity, the concept of aggregation degree is constructed to describe the diversity of population, and the particle search space is adjusted by setting the adaptive threshold of particle swarm. At the same time according to the size of the aggregation of particles re-assigned to increase the diversity of the population so that the algorithm is easier to jump out of the local optimal. Finally the above two improved algorithms are integrated into the clustering method. By improving the convergence ability of the algorithm to guide the direction of clustering, and through the detection of two groups of real data sets to verify the performance of the integrated algorithm.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18;TP311.13

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