【摘要】：為了加快蝙蝠算法的收斂速度并提高尋優(yōu)精度,提出一種動(dòng)態(tài)調(diào)整慣性權(quán)重的自適應(yīng)蝙蝠算法。該算法在速度公式中加入慣性權(quán)重,并采用一種服從均勻分布和貝塔分布的隨機(jī)調(diào)整策略,動(dòng)態(tài)地調(diào)整慣性權(quán)重的大小,以加快算法的收斂速度。另外,引入了速度糾正因子,在每次迭代時(shí),算法可根據(jù)當(dāng)前種群的迭代次數(shù)動(dòng)態(tài)地約束每一代蝙蝠的移動(dòng)步長(zhǎng),從而使算法具有一定的自適應(yīng)性。仿真實(shí)驗(yàn)結(jié)果表明,改進(jìn)后的算法的尋優(yōu)性能顯著提高,具有較快的收斂速度和較高的尋優(yōu)精度。
[Abstract]:In order to speed up the convergence speed of bat algorithm and improve the accuracy of optimization, an adaptive bat algorithm with dynamically adjusting inertia weight is proposed. In this algorithm, inertia weight is added into the velocity formula, and a random adjustment strategy of uniform distribution and beta distribution is adopted to dynamically adjust the size of inertia weight to accelerate the convergence speed of the algorithm. In addition, the speed correction factor is introduced. In each iteration, the algorithm can dynamically constrain the moving step size of each generation bat according to the number of iterations of the current population, thus making the algorithm self-adaptive. The simulation results show that the improved algorithm has better performance, faster convergence speed and higher optimization accuracy.
【作者單位】： 河南大學(xué)計(jì)算機(jī)與信息工程學(xué)院;河南大學(xué)復(fù)雜智能網(wǎng)絡(luò)系統(tǒng)研究所;河南大學(xué)軟件學(xué)院;河南大學(xué)管理科學(xué)與工程研究所;
【基金】：河南省科技廳科技攻關(guān)項(xiàng)目(162102110109) 河南省科技攻關(guān)重點(diǎn)項(xiàng)目(142102210036)資助
【分類號(hào)】：TP18

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