【摘要】：隨著人類文明的不斷進(jìn)步與發(fā)展,優(yōu)化問題已是人類生活中不可或缺的一部分。為了更好的解決此類問題,越來越多的學(xué)者致力于優(yōu)化算法的研究。近年來,多種新型的啟發(fā)式優(yōu)化算法已被提出并能更好地解決復(fù)雜的最優(yōu)化問題。其中,引力搜索算法(Gravitational Search Algorithm,GSA)在2009年由Rashedi首次提出,是一種新的啟發(fā)式優(yōu)化算法。該算法的改進(jìn)與實際應(yīng)用方面是學(xué)者們研究的主要工作。為了使引力搜索算法的優(yōu)化能力達(dá)到最佳,對該算法的改進(jìn)工作需要不斷探索與研究。因此,本論文介紹了兩種對于引力搜索算法的改進(jìn)方法,進(jìn)一步加強(qiáng)了該算法的優(yōu)化性能。主要工作有:1、針對引力搜索算法易陷入局部最優(yōu)的缺點,提出一種慣性質(zhì)量衰減的引力搜索算法。慣性質(zhì)量的衰減率由隸屬度函數(shù)定義,并給出一個新的變異算子。最后,把所提出算法應(yīng)用到經(jīng)典測試函數(shù)中,并與標(biāo)準(zhǔn)引力搜索算法及其他改進(jìn)的引力搜索算法比較,數(shù)值結(jié)果表明所給出的算法能夠提高求解精度和收斂速度。2、針對引力搜索算法的早熟收斂現(xiàn)象,提出一種自適應(yīng)變異的引力搜索算法。該算法將模擬退火算法(SA)的Metropolis準(zhǔn)則和自適應(yīng)變異概率的混合變異策略與引力搜索算法結(jié)合,數(shù)值實驗表明,改進(jìn)后的算法具有更好的性能。
[Abstract]:With the continuous progress and development of human civilization, optimization has become an indispensable part of human life. In order to solve this kind of problem better, more and more scholars devote themselves to the research of optimization algorithm. In recent years, a number of new heuristic optimization algorithms have been proposed and can better solve complex optimization problems. The Gravity search algorithm (Gravitational Search Algorithm,GSA), first proposed by Rashedi in 2009, is a new heuristic optimization algorithm. The improvement and practical application of the algorithm is the main work of scholars. In order to optimize the optimization ability of gravitational search algorithm, the improvement of the algorithm needs to be explored and studied continuously. Therefore, this paper introduces two improved methods for gravitational search algorithm, which further enhance the optimization performance of the algorithm. The main works are as follows: 1. Aiming at the disadvantage that gravity search algorithm is easy to fall into local optimum, a gravity search algorithm for inertial mass attenuation is proposed. The attenuation rate of inertial mass is defined by membership function and a new mutation operator is given. Finally, the proposed algorithm is applied to the classical test function and compared with the standard gravity search algorithm and other improved gravitational search algorithms. The numerical results show that the proposed algorithm can improve the accuracy and convergence speed of the proposed algorithm. Aiming at the premature convergence of gravitational search algorithm, an adaptive mutation gravitational search algorithm is proposed. The algorithm combines the Metropolis criterion of simulated annealing algorithm (SA) and the hybrid mutation strategy of adaptive mutation probability with the gravity search algorithm. Numerical experiments show that the improved algorithm has better performance.
【學(xué)位授予單位】：渤海大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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相關(guān)期刊論文 前4條

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本文編號：2388707