本文選題：動(dòng)態(tài)優(yōu)化 + 族群方法��； 參考：《中國(guó)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(wèn)題相比,動(dòng)態(tài)優(yōu)化問(wèn)題的特點(diǎn)是問(wèn)題的狀態(tài)(目標(biāo)函數(shù)、約束條件)隨時(shí)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(dòng)。演化算法因其具有群體搜索的特點(diǎn),適合求解一些復(fù)雜的問(wèn)題,比如動(dòng)態(tài)優(yōu)化問(wèn)題�；谧迦旱姆椒ㄊ且环N有效的演化計(jì)算技術(shù),已成為動(dòng)態(tài)優(yōu)化研究領(lǐng)域的熱點(diǎn)之一�；谧迦悍椒ǖ幕舅枷胧�,將種群劃分為若干個(gè)族群,不同族群在搜索空間的不同區(qū)域同時(shí)搜索。由于該方法允許種群同時(shí)定位多個(gè)最優(yōu)解,因此更容易實(shí)現(xiàn)對(duì)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(dòng)態(tài)優(yōu)化問(wèn)題,研究?jī)?nèi)容主要包括如下兩個(gè)方面。(1)提出了一個(gè)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(diǎn)是:用于更新種群的記憶個(gè)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(gè)族群被替換的個(gè)體數(shù)量不超過(guò)1;對(duì)提取的記憶個(gè)體分類處理,目的是在改善已有族群搜索能力的同時(shí)加強(qiáng)種群對(duì)潛在最優(yōu)區(qū)域的搜索。在MPB、CMPB、DRPBG基準(zhǔn)問(wèn)題上對(duì)該算法測(cè)試并與其它算法進(jìn)行比較,實(shí)驗(yàn)結(jié)果表明該算法是一個(gè)有競(jìng)爭(zhēng)力的動(dòng)態(tài)優(yōu)化算法。此外,實(shí)驗(yàn)部分還討論了記憶集的大小對(duì)結(jié)果的影響。(2)提出了一個(gè)應(yīng)用于動(dòng)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(diǎn)是:識(shí)別族群種子的過(guò)程只涉及部分個(gè)體而不是整個(gè)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(shí)別族群種子時(shí),本文提出了兩種確定離群點(diǎn)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(gè)基于族群的粒子群算法框架,使用該框架對(duì)psfNBC以及其它幾個(gè)有代表性的族群劃分方法在MPB問(wèn)題上測(cè)試,結(jié)果表明psfNBC可以在大多數(shù)的測(cè)試實(shí)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(guó)科學(xué)技術(shù)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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