發(fā)布時間：2018-04-18 13:54

  本文選題：動態(tài)優(yōu)化 + 族群方法��； 參考：《中國科學(xué)技術(shù)大學(xué)》2017年碩士論文

【摘要】：與一般的優(yōu)化問題相比,動態(tài)優(yōu)化問題的特點是問題的狀態(tài)(目標(biāo)函數(shù)、約束條件)隨時間變化。為了能夠快速地捕捉到環(huán)境的變化,算法需要持續(xù)地定位和追蹤最優(yōu)解的移動。演化算法因其具有群體搜索的特點,適合求解一些復(fù)雜的問題,比如動態(tài)優(yōu)化問題�；谧迦旱姆椒ㄊ且环N有效的演化計算技術(shù),已成為動態(tài)優(yōu)化研究領(lǐng)域的熱點之一�；谧迦悍椒ǖ幕舅枷胧�,將種群劃分為若干個族群,不同族群在搜索空間的不同區(qū)域同時搜索。由于該方法允許種群同時定位多個最優(yōu)解,因此更容易實現(xiàn)對全局最優(yōu)解的追蹤。本文主要研究使用基于族群的方法求解動態(tài)優(yōu)化問題,研究內(nèi)容主要包括如下兩個方面。(1)提出了一個基于族群與記憶集的混合粒子群優(yōu)化算法。該算法的特點是:用于更新種群的記憶個體的數(shù)量與族群數(shù)量相關(guān)并且隨族群數(shù)量自適應(yīng)地變化;限制每個族群被替換的個體數(shù)量不超過1;對提取的記憶個體分類處理,目的是在改善已有族群搜索能力的同時加強種群對潛在最優(yōu)區(qū)域的搜索。在MPB、CMPB、DRPBG基準(zhǔn)問題上對該算法測試并與其它算法進(jìn)行比較,實驗結(jié)果表明該算法是一個有競爭力的動態(tài)優(yōu)化算法。此外,實驗部分還討論了記憶集的大小對結(jié)果的影響。(2)提出了一個應(yīng)用于動態(tài)優(yōu)化的族群劃分方法psfNBC。與基本的Nearest-Better Clustering(NBC)算法相比,該算法的特點是:識別族群種子的過程只涉及部分個體而不是整個種群;種群按照最近種子的原則重新劃分;縮放因子φ使用隨機(jī)值而不是固定值。在識別族群種子時,本文提出了兩種確定離群點數(shù)量的方法,即固定地和自適應(yīng)地。此外,本文還給出了一個基于族群的粒子群算法框架,使用該框架對psfNBC以及其它幾個有代表性的族群劃分方法在MPB問題上測試,結(jié)果表明psfNBC可以在大多數(shù)的測試實例中取得最好的結(jié)果。
[Abstract]:Compared with the general optimization problem, the characteristics of dynamic optimization is the problem of the state (the objective function, constraint condition) change with time. In order to be able to quickly capture the changes in the environment, the algorithm needs to be continuous positioning and tracking the optimal solution of the mobile. Evolutionary algorithms because of its characteristic of population search, suitable for solving some complex the problems, such as dynamic optimization problems. The method is based on the group computing technology is an effective evolution, it has become a hot research topic in the area of dynamic optimization. The basic idea is based on the method of population, the population is divided into several groups of different ethnic groups in different regions of the search space and search. Because this method allows the population at the same time localization of multiple optimal solutions, it is easier to achieve the global optimal solution of the track. This paper mainly studies the use of dynamic optimization method for solving the problem of ethnic group based on the main research contents To include the following two aspects. (1) proposed a hybrid particle swarm optimization algorithm based on ethnicity and memory. The characteristic of this algorithm is used to update the number: individual and collective memory of population and population related changes with the number of individuals adaptively; each group was replaced by a limit of not more than 1; to classify the extracted individual memory, at the same time to improve the existing search ability in ethnic populations to strengthen the potential optimal searching area. In MPB, CMPB, DRPBG benchmark problems of the algorithm are tested and compared with other algorithms. The experimental results show that the algorithm is a competitive dynamic optimization algorithm. In addition, the experimental part of the memory set size on the results is also discussed. (2) proposed a psfNBC. group classification method is applied to the dynamic optimization and the basic Nearest-Better Clustering (NBC) algorithm. Than, the characteristic of this algorithm is: the process of identifying the seed groups involving only a part of the individual rather than the entire population; population according to the principle of seed recently re division; Phi zoom factor using random values rather than a fixed value. In recognition of ethnic seed, this paper puts forward two kinds of methods to determine from the group number, namely fixed and adaptive. In addition, this paper also gives a group based on particle swarm algorithm framework, using the framework of ethnic division method of representative test on MPB of psfNBC and several other, the results show that psfNBC can achieve the best results in most test cases.

【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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本文編號：1768640