本文選題：人工蜂群算法 + 差分進化算法��； 參考：《延安大學(xué)》2017年碩士論文

【摘要】：隨著應(yīng)用和需求的不斷擴展,非凸、非線性、高維、多變量和多目標等復(fù)雜優(yōu)化問題大量涌現(xiàn),對于這類問題的優(yōu)化,傳統(tǒng)優(yōu)化算法已不再適用.群智能優(yōu)化算法以其獨特的尋優(yōu)機制彌補了傳統(tǒng)算法的不足,被廣泛應(yīng)用于解決各領(lǐng)域的復(fù)雜優(yōu)化問題.常見的算法有遺傳算法、粒子群算法、蟻群算法、人工魚群算法等.Karaboga于2005年提出了人工蜂群算法,該算法具有簡單易實現(xiàn)、控制參數(shù)少、魯棒性強以及全局搜索能力較強等特點,因此得到了大量學(xué)者的關(guān)注,并在實際生活生產(chǎn)問題中得到廣泛應(yīng)用.本文首先簡單介紹了最優(yōu)化問題和最優(yōu)化方法的發(fā)展歷程,人工蜂群算法是一種新興的群智能優(yōu)化算法,自提出以來被大量學(xué)者關(guān)注并應(yīng)用,但該算法還處于初級階段,仍存在“早熟”收斂、局部搜索能力較弱和進化后期收斂速度較慢等缺點.本文在對算法的不足進行分析的基礎(chǔ)上,提出了一種改進的人工蜂群算法.主要工作有:(1)深入剖析了標準人工蜂群算法的理論基礎(chǔ)、基本原理、實現(xiàn)步驟等,并基于以上分析,總結(jié)出算法的優(yōu)缺點;(2)針對算法的不足,本文主要從兩方面對標準人工蜂群算法進行改進,一是采用反向?qū)W習(xí)的初始化方法,以增加解的多樣性,二是引入受差分進化算法啟發(fā)的搜索方程,以提高算法的開發(fā)能力;(3)通過仿真實驗驗證了改進后的算法具有更好的性能,優(yōu)化能力更強.
[Abstract]:With the continuous expansion of application and demand, many complex optimization problems, such as non convex, nonlinear, high dimension, multivariable and multi-objective, are emerging. The traditional optimization algorithm is no longer applicable to the optimization of this kind of problem. The swarm intelligence optimization algorithm makes up for the shortcomings of the traditional algorithm with its unique optimization mechanism, and is widely used to solve the complexity of various fields. The common algorithms are genetic algorithm, particle swarm optimization, ant colony algorithm, artificial fish swarm algorithm and so on. In 2005,.Karaboga proposed artificial bee colony algorithm. This algorithm has the characteristics of simple and easy realization, low control parameters, strong robustness and strong global search ability, so a large number of scholars pay attention to it and live in real life. This paper first briefly introduces the development of optimization and optimization. Artificial bee colony algorithm is a new swarm intelligence optimization algorithm. Since it is put forward, a large number of scholars pay attention to it and apply it, but the algorithm is still in the primary stage, still has the convergence of "early maturing", and the local search ability is weak. In this paper, based on the analysis of the shortcomings of the algorithm, an improved artificial bee colony algorithm is proposed in this paper. The main work is as follows: (1) the theoretical basis, basic principle, and implementation steps of the standard artificial bee colony algorithm are analyzed in depth, and the advantages and disadvantages of the algorithm are summarized based on the above analysis; (2) the needles are summarized. For the deficiency of the algorithm, this paper mainly improves the standard artificial bee colony algorithm from two parties. First, the initialization method of reverse learning is adopted to increase the diversity of the solution. The two is to introduce the search equation inspired by the differential evolution algorithm to improve the development ability of the algorithm. (3) the improved algorithm has been proved to be better by simulation experiments. The performance, the optimization ability is stronger.
【學(xué)位授予單位】：延安大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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