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  本文關(guān)鍵詞：群體協(xié)作的果蠅優(yōu)化算法及其在Web服務(wù)組合中的應(yīng)用研究　出處：《安徽大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 果蠅優(yōu)化算法 群體協(xié)作 搜索系數(shù) 收斂精度 Web服務(wù)組合

【摘要】：果蠅優(yōu)化算法(Fruit Fly Optimization Algorithm, FOA)是模仿果蠅在覓食過程中的合作行為而提出的一種新型群智能優(yōu)化算法。FOA算法的原理是根據(jù)每只果蠅隨機(jī)飛行到的位置計(jì)算各自的味道濃度值,并找出最佳味道濃度值,然后不斷迭代,最終找到食物,實(shí)現(xiàn)優(yōu)化問題的求解。FOA具有計(jì)算過程簡單和易于理解的優(yōu)點(diǎn),然而FOA也存在一些缺點(diǎn),例如,FOA使用固定步長容易導(dǎo)致算法的局部搜索能力和全局搜索能力失去平衡；FOA初始位置的選擇對算法的穩(wěn)定性造成了很大影響。本文針對FOA存在的缺點(diǎn),對基本果蠅優(yōu)化算法進(jìn)行改進(jìn)以提高算法的尋優(yōu)性能,提出了群體協(xié)作的果蠅優(yōu)化算法(Collaborative Swarm Fruit Fly Optimization Algorithm, CSFOA),并將CSFOA應(yīng)用于求解Web服務(wù)組合問題中,本文的主要研究工作如下：(1)針對FOA的不足,本文提出一種群體協(xié)作的果蠅優(yōu)化算法。首先,CSFOA采用雙種群的協(xié)作機(jī)制和遞減步長的策略,有效提高了算法的尋優(yōu)精度和收斂速度。其次,CSFOA使用搜索系數(shù)h控制初始果蠅群體位置的選擇以提高算法的收斂穩(wěn)定性。(2)將CSFOA應(yīng)用于連續(xù)型函數(shù)優(yōu)化問題,并對18個經(jīng)典的Benchmark函數(shù)進(jìn)行測試,并與經(jīng)典的群智能優(yōu)化算法進(jìn)行了大量對比。實(shí)驗(yàn)結(jié)果表明,CSFOA從整體上比FOA具有更好的全局搜索能力、更快的收斂速度、更高的收斂精度和穩(wěn)定性。同時,與IFOA、PSO、DE相比,特別在高維函數(shù)求解方面,CSFOA具有更高的尋優(yōu)精度和穩(wěn)定性。(3)為更全面驗(yàn)證CSFOA的實(shí)際應(yīng)用能力,將CSFOA應(yīng)用于求解離散型的Web服務(wù)組合問題。通過果蠅的位置信息找到工作流中各個Web服務(wù)的位置,最后使用適應(yīng)度函數(shù)計(jì)算組合服務(wù)質(zhì)量的高低。將CSFOA的實(shí)驗(yàn)結(jié)果與FOA、PSO和DE的結(jié)果進(jìn)行對比分析,實(shí)驗(yàn)結(jié)果證明,CSFOA具有更好的求解精度和求解速度；同時,穩(wěn)定性上CSFOA優(yōu)于PSO和DE。
[Abstract]:Fruit Fly Optimization Algorithm. FOAA). A new swarm intelligence optimization algorithm, FOA algorithm, is proposed to simulate the cooperative behavior of Drosophila during foraging. The principle of FOA algorithm is to calculate the taste concentration of each fly based on the random flight position. And find out the best taste concentration, and then iterate, finally find food, to achieve the optimization problem solving. FOA has the advantages of simple and easy to understand the calculation process, but FOA also has some shortcomings. For example, using fixed step size of FOA can lead to the imbalance of local search ability and global search ability of the algorithm. The selection of the initial position of FOA has a great influence on the stability of the algorithm. Aiming at the shortcomings of FOA, the basic Drosophila optimization algorithm is improved to improve the performance of the algorithm. A collaborative Swarm Fruit Fly Optimization Algorithm is proposed. CSFOAA, and applies CSFOA to solve the Web service composition problem, the main research work of this paper is as follows: 1) aiming at the shortage of FOA. In this paper, we propose an optimization algorithm for Drosophila. Firstly, CSFOA adopts the cooperation mechanism of two populations and the strategy of decreasing step size, which can effectively improve the optimization accuracy and convergence speed of the algorithm. CSFOA uses the search coefficient h to control the initial Drosophila population location to improve the convergence stability of the algorithm. The CSFOA is applied to the continuous function optimization problem. The 18 classical Benchmark functions are tested and compared with the classical swarm intelligence optimization algorithm. The experimental results show that. CSFOA has better global search ability, faster convergence speed, higher convergence accuracy and stability than FOA. At the same time, compared with FOA PSODE. Especially in the aspect of solving high-dimensional function, CSFOA has higher precision and stability of optimization. It is more comprehensive to verify the practical application ability of CSFOA. The CSFOA is applied to solve the discrete Web service composition problem, and the location of each Web service in workflow is found by the location information of Drosophila. Finally, the fitness function is used to calculate the quality of service composition. The experimental results of CSFOA are compared with those of CSFOA and DE, and the experimental results are proved. CSFOA has better accuracy and speed. At the same time, the stability of CSFOA is better than that of PSO and De.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP18;TP393.09

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本文編號：1395210