發(fā)布時(shí)間：2018-05-31 05:26

  本文選題：多元宇宙優(yōu)化算法 + 指數(shù)步長(zhǎng)　； 參考：《廣西民族大學(xué)》2017年碩士論文

【摘要】：多元宇宙優(yōu)化算法是受自然界中多元宇宙現(xiàn)象的啟發(fā)而提出的一種元啟發(fā)式算法,該算法結(jié)構(gòu)簡(jiǎn)單、參數(shù)少易于理解且具有較強(qiáng)的搜索能力。近年來,該算法受到了國內(nèi)外學(xué)者的極大關(guān)注。本論文針對(duì)多元宇宙優(yōu)化算法所存在的一些不足,從指數(shù)步長(zhǎng)增長(zhǎng)和位置更新策略等方面進(jìn)行改進(jìn),目的在于改進(jìn)多元宇宙算法的整體性能,完善算法的理論基礎(chǔ),并將改進(jìn)后的算法應(yīng)用于函數(shù)優(yōu)化,旅行商問題和聚類分析等,從而拓寬其應(yīng)用領(lǐng)域。本文的工作主要包括以下3個(gè)方面:(1)采用指數(shù)步長(zhǎng)增長(zhǎng)的方式以增強(qiáng)算法的深度搜索性能,通過引入膨脹系數(shù)參數(shù)可以有效地縮小解的空間,使算法具有更快的搜索速度,同時(shí)也促使種群向最優(yōu)個(gè)體附近迅速靠攏,使得算法在提高精度的同時(shí)加快了收斂速度。(2)針對(duì)傳統(tǒng)方法在求解聚類分析問題時(shí)精度低,聚類效率不高,易陷入局部最優(yōu)等問題,本文采用指數(shù)步長(zhǎng)增長(zhǎng)的方式加強(qiáng)算法局部搜索能力,提高了算法的執(zhí)行效率,優(yōu)化了多元宇宙算法對(duì)數(shù)據(jù)集的聚類分析能力。實(shí)驗(yàn)仿真結(jié)果表明,改進(jìn)過的多元宇宙算法在求解聚類分析問題中是可行和高效的。(3)針對(duì)傳統(tǒng)算法在求解城市數(shù)量較大的旅行商問題時(shí)運(yùn)行速度較慢,在多元宇宙優(yōu)化算法的基礎(chǔ)上引入指數(shù)步長(zhǎng)增長(zhǎng)的方式,在求解旅行商問題可以加快搜索速度,并對(duì)求解過程中出現(xiàn)的異常解后及時(shí)進(jìn)行修正,可更高效地對(duì)旅行商問題進(jìn)行求解。
[Abstract]:The multi-universe optimization algorithm is a meta-heuristic algorithm inspired by the phenomenon of multi-universe in nature. The algorithm is simple in structure, easy to understand with few parameters and has strong searching ability. In recent years, the algorithm has attracted great attention from scholars at home and abroad. In order to improve the overall performance of the multiverse algorithm and improve the theoretical basis of the algorithm, this paper aims at improving the exponential step size growth and location updating strategy in view of the shortcomings of the multiverse optimization algorithm. The improved algorithm is applied to function optimization, traveling salesman problem and clustering analysis. The work of this paper mainly includes the following three aspects: 1) the method of exponential step growth is adopted to enhance the depth search performance of the algorithm. By introducing the expansion coefficient parameter, the space of the solution can be reduced effectively, and the search speed of the algorithm is faster. At the same time, it also makes the population close to the optimal individual rapidly, which makes the algorithm improve the accuracy and speed of convergence. (2) aiming at the problems of low precision, low clustering efficiency and easy to fall into the local optimum, the traditional methods can solve the cluster analysis problem with low precision, low clustering efficiency, and easy to fall into local optimization. In this paper, the exponential step size is used to enhance the local search ability of the algorithm, to improve the efficiency of the algorithm, and to optimize the clustering analysis ability of the multiverse algorithm to the data set. The experimental results show that the improved multiverse algorithm is feasible and efficient in clustering analysis. On the basis of the multi-universe optimization algorithm, the exponential step growth method is introduced. The search speed can be accelerated by solving the traveling salesman problem, and the abnormal solutions in the process of solving the problem can be corrected in time. The traveling salesman problem can be solved more efficiently.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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本文編號(hào)：1958465