發(fā)布時(shí)間：2018-07-10 11:41

  本文選題：群體智能 + 蝙蝠算法��； 參考：《北京工業(yè)大學(xué)》2016年博士論文

【摘要】：群智能優(yōu)化算法是一類模擬動(dòng)物群體行為的隨機(jī)優(yōu)化算法,該類算法利用動(dòng)物群體所突顯的智能來求解問題。目前為止,已經(jīng)提出了很多群智能優(yōu)化算法,這些算法從動(dòng)物的視覺、聽覺、嗅覺等角度出發(fā)來求解問題,但它們普遍存在局部搜索性能較差,以及算法過早收斂等問題。而蝙蝠則具有一種完全不同的覓食方式—回聲定位。因此,模擬這種覓食方式的蝙蝠算法對(duì)上述問題提供了一種完全不同的解決方式。本論文以蝙蝠算法為基本算法,研究高效的改進(jìn)蝙蝠算法,以期進(jìn)一步改善蝙蝠算法性能使其應(yīng)用范圍更廣闊。與已有蝙蝠算法的研究成果相比較,本論文系統(tǒng)地從性能分析、理論分析、算子設(shè)計(jì)、算法融合、知識(shí)學(xué)習(xí)、多目標(biāo)優(yōu)化等方面進(jìn)行研究,并將蝙蝠算法應(yīng)用在生物信息學(xué)、無線傳感器網(wǎng)絡(luò)等多個(gè)工程問題中。本文研究成果和創(chuàng)新點(diǎn)如下:(1)針對(duì)蝙蝠算法中速度震蕩問題,引入速度權(quán)重概念,設(shè)計(jì)了不同的速度權(quán)重,使蝙蝠個(gè)體能根據(jù)其適應(yīng)值自適應(yīng)的調(diào)整飛行速度,更好的在搜索空間內(nèi)進(jìn)行搜索;針對(duì)標(biāo)準(zhǔn)蝙蝠算法搜索方式發(fā)散和搜索區(qū)域不完整的缺點(diǎn),通過擴(kuò)大頻率范圍,使蝙蝠的搜索區(qū)域覆蓋整個(gè)搜索空間,提高了蝙蝠算法的全局搜索能力。(2)針對(duì)蝙蝠算法局部搜索能力差的問題,從算法融合角度去優(yōu)化標(biāo)準(zhǔn)蝙蝠算法,分析不同策略與智能算法的優(yōu)化機(jī)理,從純數(shù)學(xué)理論和其它智能優(yōu)化算法中汲取優(yōu)點(diǎn),將其與蝙蝠算法相結(jié)合以進(jìn)一步提高算法性能。數(shù)學(xué)理論方面,利用Powell法增強(qiáng)標(biāo)準(zhǔn)蝙蝠算法的局部搜索能力。智能優(yōu)化算法融合方面,本文分析并采納了遺傳算法、模擬退火算法和分布估計(jì)算法的優(yōu)異算子,將其引進(jìn)到蝙蝠算法中進(jìn)行融合,從而進(jìn)一步優(yōu)化蝙蝠算法。(3)針對(duì)蝙蝠算法個(gè)體信息利用率低的缺點(diǎn),將知識(shí)學(xué)習(xí)引入到蝙蝠算法中。首先,蝙蝠個(gè)體在尋優(yōu)過程中不斷利用其歷史知識(shí)和群體知識(shí)調(diào)整優(yōu)化,有利于群體成員向更好的方向移動(dòng),加快算法收斂速度。其次,針對(duì)高維多峰問題,利用相似度聚類函數(shù)將蝙蝠群體分為不同的蝙蝠簇,使蝙蝠有針對(duì)性的進(jìn)行區(qū)域知識(shí)學(xué)習(xí)。最后,引入偏好知識(shí)維度概念,提高蝙蝠個(gè)體學(xué)習(xí)能力。(4)在多目標(biāo)蝙蝠算法的基礎(chǔ)上引入非支配快速排序策略,該策略不僅可以篩選出距離真實(shí)前沿較近的個(gè)體,而且可以使得所求個(gè)體均勻的分布在真實(shí)前沿的邊緣。偏好多面體策略的引入進(jìn)一步降低了決策者在選擇非劣解時(shí)的困難,該策略利用非減擬凹函數(shù)代替決策者參與非劣解的篩選,所得的非劣解可以有效的反映決策者的偏好。(5)將蝙蝠算法應(yīng)用到多個(gè)不同領(lǐng)域,其中包括生物信息學(xué)中的RNA二級(jí)結(jié)構(gòu)預(yù)測(cè)問題、蛋白質(zhì)折疊預(yù)測(cè)問題,無線傳感器網(wǎng)絡(luò)中的覆蓋問題、定位問題以及團(tuán)簇優(yōu)化問題。針對(duì)不同問題,對(duì)蝙蝠算法離散化,并設(shè)計(jì)了不同目標(biāo)優(yōu)化模型。
[Abstract]:Swarm intelligence optimization algorithm is a kind of stochastic optimization algorithm for simulating animal group behavior. This kind of algorithm uses the intelligence of animal groups to solve the problem. So far, many swarm intelligence optimization algorithms have been proposed. These algorithms solve the problem from the angle of animal vision, hearing and smell, but they are generally local. The bats have a completely different foraging method - echolocation. Therefore, the bat algorithm that simulates this way of foraging provides a completely different solution to the above problem. This paper uses bat algorithm as the basic algorithm to study the efficient bat algorithm. In order to further improve the performance of bat algorithm to make its application wider, compared with the achievements of the existing bat algorithm, this paper systematically studies the performance analysis, theoretical analysis, operator design, algorithm fusion, knowledge learning, multi-objective optimization and so on, and applies the bat algorithm to bioinformatics and wireless sensor networks. The research results and innovation points of this paper are as follows: (1) according to the speed shock problem in bat algorithm, the speed weight concept is introduced to design different speed weights, so that the bat individual can adjust the flight speed adaptively according to its adaptive value, search in the search space better, search for the standard bat algorithm. By expanding the frequency range, the search area of bats is covered with the whole search space, and the global search ability of the bat algorithm is improved by expanding the frequency range. (2) in view of the problem of the poor local search ability of the bat algorithm, the standard bat algorithm is optimized from the angle of algorithm fusion, and the different strategies and intelligent calculation are analyzed. The optimization mechanism of the method is derived from the pure mathematical theory and other intelligent optimization algorithms. The algorithm is combined with the bat algorithm to further improve the performance of the algorithm. In mathematical theory, the Powell method is used to enhance the local search ability of the standard bat algorithm. The intelligent optimization algorithm combines the square surface. This paper analyzes and adopts the genetic algorithm, and simulated the regression. The excellent operators of fire algorithm and distribution estimation algorithm are introduced into the bat algorithm to further optimize the bat algorithm. (3) the knowledge learning is introduced into bat algorithm for the disadvantage of low utilization rate of individual information in bat algorithm. First, the bat individuals continue to use their historical knowledge and group knowledge in the process of optimization. The adjustment and optimization will help the group members to move in a better direction and speed up the convergence speed of the algorithm. Secondly, according to the Gao Weiduo peak problem, the bat group is divided into different bat clusters by similarity clustering function, so that the bat can learn the regional knowledge pertinence. Finally, the concept of preference knowledge dimension is introduced to improve the individual learning ability of the bat. (4) on the basis of the multi target bat algorithm, the non dominated fast sorting strategy is introduced. This strategy can not only screen out individuals near the real front of the real distance, but also make the individual distributed evenly on the edge of the real frontier. In this strategy, the non substandard concave function is used instead of the decision maker to select the non inferior solutions. The non inferior solutions can effectively reflect the preference of the decision-makers. (5) the bat algorithm is applied to a number of different fields, including the RNA two structure prediction in bioinformatics, the problem of protein folding prediction and the coverage in Wireless Sensor Networks For different problems, the bat algorithm is discretized and different target optimization models are designed.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18

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本文編號(hào)：2113281