本文選題：猴群算法　切入點(diǎn)：傳感器優(yōu)化布置　出處：《蘭州交通大學(xué)》2016年碩士論文

【摘要】：傳感器的優(yōu)化布置是一類典型的組合優(yōu)化問題。目前,傳感器優(yōu)化布置的方法有多種,但都存在各自的缺點(diǎn)。猴群算法是近年提出的一種智能仿生算法,適用于求解多變量、多峰值的函數(shù)優(yōu)化問題。利用猴群算法進(jìn)行傳感器的優(yōu)化布置,是目前國(guó)內(nèi)外學(xué)者廣泛關(guān)注和研究的熱點(diǎn)問題之一。本文在總結(jié)猴群算法國(guó)內(nèi)外研究現(xiàn)狀及成果的前提下,對(duì)猴群算法進(jìn)行了相應(yīng)的改進(jìn),使其適應(yīng)傳感器優(yōu)化布置的需要。本文研究的內(nèi)容如下:(1)介紹了傳感器優(yōu)化布置的意義,對(duì)猴群算法的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行了綜述,總結(jié)了猴群算法的研究成果,給出了猴群算法改進(jìn)和提高的方向,確立了傳感器優(yōu)化布置的數(shù)學(xué)模型。(2)針對(duì)猴群算法初始化種群隨機(jī)性大、固定爬步長(zhǎng)不利于搜索局部最優(yōu)解的問題,提出了一種改進(jìn)的猴群算法。該算法以MAC矩陣(Modal Assurance Criterion,模態(tài)置信矩陣)作為目標(biāo)函數(shù),通過正態(tài)分布的方法構(gòu)造初始種群來增強(qiáng)猴群的多樣性;采用自適應(yīng)的變動(dòng)爬步長(zhǎng),提高算法運(yùn)行的速度和求解精度。(3)針對(duì)猴群算法跳區(qū)間固定、優(yōu)秀猴子特征信息不能傳承等缺陷,提出了野草猴群算法。該算法在改進(jìn)的猴群算法基礎(chǔ)上,采用自適應(yīng)的跳過程,并引入以適應(yīng)度為基準(zhǔn)的野草繁殖進(jìn)化和競(jìng)爭(zhēng)生存機(jī)制,解決了優(yōu)秀猴子特征信息的傳承問題,進(jìn)一步提高算法的求解精度。(4)猴群算法在附近區(qū)域進(jìn)行最優(yōu)解的搜尋時(shí),難免存在搜索盲區(qū),易導(dǎo)致某些最優(yōu)解隱藏在步長(zhǎng)覆蓋的區(qū)域錯(cuò)失“良機(jī)”,降低了算法搜尋全局最優(yōu)解的能力。針對(duì)該問題提出了基于蜂群采蜜行為的猴群算法。該算法在改進(jìn)的猴群算法基礎(chǔ)上,引入蜂群算法的采蜜行為,利用蜂群搜尋機(jī)制對(duì)所有區(qū)域進(jìn)行搜索后,再將初步遴選出來的猴子進(jìn)行猴群算法的基本搜索,改善了算法的搜索性能。(5)用8個(gè)測(cè)試函數(shù)及常用算法分別對(duì)上述3種改進(jìn)后的算法進(jìn)行測(cè)試分析,結(jié)果表明,改進(jìn)后的猴群算法求解精度和收斂速度都得到了提高,算法性能改善明顯。(6)建立了糊底機(jī)涂膠機(jī)構(gòu)算例的有限元模型,通過上述3種改進(jìn)后的算法對(duì)其進(jìn)行傳感器的優(yōu)化布置方案選擇,并對(duì)它們的特點(diǎn)進(jìn)行了橫向?qū)Ρ取?br/>[Abstract]:The optimal arrangement of sensors is a typical combinatorial optimization problem.At present, there are many methods for optimizing sensor layout, but each has its own shortcomings.Monkey swarm algorithm is an intelligent bionic algorithm proposed in recent years, which is suitable for solving multivariable and multi-peak function optimization problems.The optimal arrangement of sensors using monkey swarm algorithm is one of the hot issues that scholars at home and abroad pay close attention to.On the premise of summarizing the research status and achievements of monkey swarm algorithm at home and abroad, this paper improves the algorithm to meet the needs of optimal sensor layout.The contents of this paper are as follows: (1) the significance of sensor optimization is introduced, the research status of monkey swarm algorithm at home and abroad is summarized, the research results of monkey swarm algorithm are summarized, and the direction of improvement and improvement of monkey swarm algorithm is given.The mathematical model of optimal sensor placement is established. (2) aiming at the problem that the initialization of the population is random and the fixed crawling step is not conducive to searching for the local optimal solution, an improved monkey swarm algorithm is proposed.In this algorithm, the MAC matrix Modal Assurance criteria (modal confidence matrix) is taken as the objective function, the initial population is constructed by normal distribution method to enhance the diversity of the monkey group, and the adaptive variable crawling step is used to enhance the diversity of the monkey group.To improve the speed and accuracy of the algorithm, a wild grass monkey swarm algorithm is proposed to solve the problems of fixed jump interval of monkey swarm algorithm, and the excellent monkey characteristic information can not be passed on.Based on the improved monkey swarm algorithm, the adaptive jump process is adopted, and the mechanism of weed propagation evolution and competitive survival based on fitness is introduced to solve the problem of the transmission of excellent monkey characteristic information.Further improving the accuracy of the algorithm. (4) when the monkey swarm algorithm searches for the optimal solution in the nearby area, it is inevitable that there are blind areas, which can easily lead to the missing "opportunity" of some optimal solutions hidden in the region covered by the step size.The ability of the algorithm to search for the global optimal solution is reduced.To solve this problem, a monkey colony algorithm based on honeybee behavior is proposed.On the basis of the improved monkey swarm algorithm, the honey collecting behavior of the bee colony algorithm is introduced. After all the regions are searched by the bee colony search mechanism, the monkeys selected from the preliminary algorithm are searched for the basic search of the monkey swarm algorithm.The search performance of the improved algorithm is improved. (5) eight test functions and common algorithms are used to test and analyze the above three improved algorithms. The results show that the precision and convergence speed of the improved monkey swarm algorithm are improved.The finite element model of the glue coating mechanism of the paste machine is established. Through the above three improved algorithms, the optimal arrangement scheme of the sensor is selected, and their characteristics are compared horizontally.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18;TP212

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