【摘要】：針對(duì)人工魚群算法存在易陷入局部最優(yōu)、魯棒性差以及尋優(yōu)精度低的問題,提出了精英學(xué)習(xí)的多維動(dòng)態(tài)自適應(yīng)人工魚群算法.傳統(tǒng)人工魚群用歐式距離度量視野、步長(zhǎng),無法體現(xiàn)不同維度上魚群的搜索進(jìn)度.提出的算法為每個(gè)維度設(shè)定獨(dú)立的視野和步長(zhǎng),從而定義了視野向量、步長(zhǎng)矩陣及多維鄰域,以此改進(jìn)了魚群的4種基本行為,使人工魚個(gè)體能夠根據(jù)魚群分布情況自適應(yīng)調(diào)整尋優(yōu)范圍.為了增加魚群的全局性,降低人工魚陷入局部最優(yōu)的可能性,提出了一種人工魚精英學(xué)習(xí)策略.仿真實(shí)驗(yàn)結(jié)果表明,該算法能有效地提高人工魚群的尋優(yōu)精度、尋優(yōu)質(zhì)量及魯棒性,且提高了人工魚群的全局搜索能力.
[Abstract]:In order to solve the problem that artificial fish swarm algorithm is easy to fall into local optimization, poor robustness and low optimization accuracy, a multi-dimensional dynamic adaptive artificial fish swarm algorithm based on elite learning is proposed. Traditional artificial fish stocks use Euclidean distance to measure visual field and step size, which can not reflect the search progress of fish stocks in different dimensions. The proposed algorithm sets an independent field of vision and step size for each dimension, thus defining the field vector, step size matrix and multi-dimensional neighborhood, which improves the four basic behaviors of fish stocks and enables artificial fish individuals to adaptively adjust the optimization range according to the distribution of fish stocks. In order to increase the global nature of fish stocks and reduce the possibility of artificial fish falling into local optimization, an elite learning strategy for artificial fish is proposed. The simulation results show that the algorithm can effectively improve the optimization accuracy, optimization quality and robustness of artificial fish stocks, and improve the global search ability of artificial fish stocks.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院;
【基金】：國(guó)家“八六三”高技術(shù)研究發(fā)展計(jì)劃項(xiàng)目(2014AA041505)資助 國(guó)家自然科學(xué)基金項(xiàng)目(61572238)資助
【分類號(hào)】：TP18
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本文編號(hào)：2505925