本文選題：人工蜂群算法 + 高維混沌系統(tǒng)��； 參考：《智能系統(tǒng)學(xué)報(bào)》2017年05期

【摘要】：針對(duì)人工蜂群算法初始化群體分布不均勻和局部搜索能力弱的問(wèn)題,本文提出了一種增強(qiáng)局部搜索能力的人工蜂群算法(ESABC)。首先,在種群初始化階段采用高維洛倫茲混沌系統(tǒng),得到遍歷性好、有規(guī)律的初始群體,避免了隨機(jī)初始化的盲目性。然后,采用基于對(duì)數(shù)函數(shù)的適應(yīng)度評(píng)價(jià)方式,以增大種群個(gè)體間差異,減小選擇壓力,避免過(guò)早收斂。最后,在微分進(jìn)化算法的啟發(fā)下,提出了一種新的搜索策略,采用當(dāng)前種群中的最佳個(gè)體來(lái)引導(dǎo)下一代的更新,以提高算法的局部搜索能力。通過(guò)對(duì)12個(gè)經(jīng)典測(cè)試函數(shù)的仿真實(shí)驗(yàn),并與其他經(jīng)典的改進(jìn)人工蜂群算法對(duì)比,結(jié)果表明:本文算法具有良好的尋優(yōu)性能,無(wú)論在解的精度還是收斂速度方面效果都有所提高。
[Abstract]:In order to solve the problem of uneven population distribution and weak local search ability of artificial bee colony algorithm, an artificial bee colony algorithm is proposed to enhance the local search ability. Firstly, the high dimensional Lorentz chaotic system is used in the initial stage of population initialization, and a good ergodicity and regular initial population is obtained, which avoids the blindness of random initialization. Then, a logarithmic function based fitness evaluation method is used to increase the individual population differences, reduce the selection pressure and avoid premature convergence. Finally, under the inspiration of differential evolution algorithm, a new search strategy is proposed, in which the best individuals in the current population are used to guide the next generation update, so as to improve the local search ability of the algorithm. Through the simulation of 12 classical test functions, and compared with other classical improved artificial bee colony algorithms, the results show that the proposed algorithm has a good performance of optimization, both the accuracy of the solution and the convergence rate are improved.
【作者單位】： 華僑大學(xué)工學(xué)院;華僑大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61203242) 物聯(lián)網(wǎng)云計(jì)算平臺(tái)建設(shè)資助項(xiàng)目(2013H2002) 華僑大學(xué)研究生科研創(chuàng)新能力培育計(jì)劃資助項(xiàng)目(1511322003)
【分類號(hào)】：TP18

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本文編號(hào)：1989563