本文選題：果蠅優(yōu)化算法 + 局部最優(yōu)��； 參考：《計(jì)算機(jī)工程》2017年02期

【摘要】：在傳統(tǒng)果蠅優(yōu)化算法中,果蠅的新位置常被限定在特定區(qū)域內(nèi),因此,尋優(yōu)結(jié)果對(duì)搜索半徑依賴性強(qiáng),導(dǎo)致算法極易陷入局部最優(yōu)。為此,提出一種改進(jìn)的果蠅優(yōu)化算法。將果蠅在每個(gè)維度上的搜索范圍分為2個(gè)部分,給出近郊區(qū)和遠(yuǎn)郊區(qū)的概念,引入局部最優(yōu)導(dǎo)向因子,通過(guò)動(dòng)態(tài)調(diào)整該因子協(xié)調(diào)果蠅在不同區(qū)域的搜索強(qiáng)度,通過(guò)隨機(jī)選擇果蠅位置向量中特定維度實(shí)現(xiàn)果蠅位置更新。仿真實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)自適應(yīng)混沌果蠅優(yōu)化算法相比,該算法能有效避免搜尋半徑的影響,且在收斂精度、收斂速度等方面具有明顯優(yōu)勢(shì)。
[Abstract]:In the traditional Drosophila optimization algorithm, the new position of Drosophila is often confined to a specific region. Therefore, the search results are strongly dependent on the search radius, resulting in the algorithm is prone to fall into local optimization. Therefore, an improved algorithm for fruit fly optimization is proposed. The search range of Drosophila on each dimension is divided into two parts. The concepts of peri-suburb and far-suburb are given, and the local optimal guidance factor is introduced. The search intensity of Drosophila in different regions is coordinated by dynamically adjusting the factor. Drosophila position was updated by random selection of specific dimensions in the Drosophila position vector. The simulation results show that compared with the traditional adaptive chaotic Drosophila optimization algorithm, the algorithm can effectively avoid the influence of searching radius, and has obvious advantages in convergence accuracy and convergence speed.
【作者單位】： 中央財(cái)經(jīng)大學(xué)信息學(xué)院;吉林大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：信息保障技術(shù)重點(diǎn)實(shí)驗(yàn)室開放基金(KJ-14-008)
【分類號(hào)】：TP18
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本文編號(hào)：1833435