化算法：酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO))
智能優(yōu)化算法酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO)文章目錄智能優(yōu)化算法酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO)1.算法原理1.1 初始化1.2 迭代階段2.實驗結(jié)果3.MatlabPython4.參考文獻酶作用優(yōu)化算法EAO是模擬生物體內(nèi)酶自適應(yīng)催化機制的仿生元啟發(fā)式優(yōu)化算法。靈感來源于酶的催化行為酶作為一類特殊蛋白質(zhì)與靶分子結(jié)合后會發(fā)生構(gòu)象變化降低化學(xué)反應(yīng)活化能、加快反應(yīng)速率且酶本身不會被消耗。在輔因子與環(huán)境信號共同作用下酶促進底物轉(zhuǎn)化的自適應(yīng)機制構(gòu)成了EAO算法的設(shè)計基礎(chǔ)。1.算法原理1.1 初始化和其他群優(yōu)化算法一樣采用隨機初始化X i ( 0 ) L B ( U B ? L B ) ⊙ r i , \mathbf{X}_i^{(0)} \mathbf{LB} (\mathbf{UB}-\mathbf{LB}) \odot \mathbf{r}_i,Xi(0)?LB(UB?LB)⊙ri?,X n [ x 1 , 1 x 1 , 2 … x 1 , D i m x 2 , 1 x 2 , 2 … x 2 , D i m ? ? ? ? x N , 1 x N , 2 … x N , D i m ] N × D i m X_{n} \begin{bmatrix} x_{1,1} x_{1,2} \dots x_{1,Dim} \\ x_{2,1} x_{2,2} \dots x_{2,Dim} \\ \vdots \vdots \ddots \vdots \\ x_{N,1} x_{N,2} \dots x_{N,Dim} \end{bmatrix}_{N \times Dim}Xn??x1,1?x2,1??xN,1??x1,2?x2,2??xN,2??……?…?x1,Dim?x2,Dim??xN,Dim???N×Dim?f b e s t min ? ( f ( X i ) ) f_{best} \min\left(f\left(X_i\right)\right)fbest?min(f(Xi?))X s u p e r i o r X ( arg ? min ? ( f ( X i ) ) ) X_{superior} X\left(\arg\min\left(f\left(X_i\right)\right)\right)Xsuperior?X(argmin(f(Xi?)))定義迭代t tt時刻的自適應(yīng)因子AFA F t t M a x I t e r . \mathrm{AF}_t\sqrt{\frac{t}{\mathrm{MaxIter}}}.AFt?MaxItert??.1.2 迭代階段每一次迭代t tt每個底物個體生成兩個候選位置。第一個底物候選位置更新公式X i , 1 ( t ) ( X b e s t ( t ? 1 ) ? X i ( t ? 1 ) ) ρ i ⊙ sin ? ( A F t ? X i ( t ? 1 ) ) , \mathbf{X}_{i,1}^{(t)} \left(\mathbf{X}_{\mathrm{best}}^{(t-1)}-\mathbf{X}_i^{(t-1)}\right) \boldsymbol{\rho}_i \odot \sin\left(\mathrm{AF}_t \cdot \mathbf{X}_\mathrm{i}^{(t-1)}\right),Xi,1(t)?(Xbest(t?1)??Xi(t?1)?)ρi?⊙sin(AFt??Xi(t?1)?),隨機選取兩個不同底物p pp、q qq計算二者距離向量d \mathbfbextvpldd X p ( t ? 1 ) ? X q ( t ? 1 ) , \mathbfbextvpl \mathbf{X}_p^{(t-1)}-\mathbf{X}_q^{(t-1)},dXp(t?1)??Xq(t?1)?,第二個底物候選位置更新公式X i , 2 ( t ) X i ( t ? 1 ) s c 1 d 1 A F t s c 2 ( X b e s t ( t ? 1 ) ? X i ( t ? 1 ) ) \mathbf{X}_{i,2}^{(t)} \mathbf{X}_i^{(t-1)} \mathrm{sc}_1 \mathbfbextvpl_1 \mathrm{AF}_t \mathrm{sc}_2 \left(\mathbf{X}_{\mathrm{best}}^{(t-1)}-\mathbf{X}_i^{(t-1)}\right)Xi,2(t)?Xi(t?1)?sc1?d1?AFt?sc2?(Xbest(t?1)??Xi(t?1)?)全局最優(yōu)個體更新規(guī)則i f F ( X i ( t ) ) F b e s t ( t ? 1 ) ? X b e s t ( t ) X i ( t ) , F b e s t ( t ) F ( X i ( t ) ) . \mathrm{if}\ F(\mathbf{X}_i^{(t)}) F_{\mathrm{best}}^{(t-1)} \implies \mathbf{X}_{\mathrm{best}}^{(t)}\mathbf{X}_i^{(t)},\ F_{\mathrm{best}}^{(t)}F(\mathbf{X}_i^{(t)}).ifF(Xi(t)?)Fbest(t?1)??Xbest(t)?Xi(t)?,Fbest(t)?F(Xi(t)?).2.實驗結(jié)果3.MatlabPython4.參考文獻[1] Rodan, A., Al?Tamimi, AK., Al?Alnemer, L. et al. Enzyme action optimizer: a novel bio?inspired optimization algorithm. J Supercomput 81, 686 (2025). 智能優(yōu)化算法：酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO)
智能優(yōu)化算法酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO)文章目錄智能優(yōu)化算法酶作用優(yōu)化算法 Enzyme Action Optimizer (EAO)1.算法原理1.1 初始化1.2 迭代階段2.實驗結(jié)果3.MatlabPython4.參考文獻酶作用優(yōu)化算法EAO是模擬生物體內(nèi)酶自適應(yīng)催化機制的仿生元啟發(fā)式優(yōu)化算法。靈感來源于酶的催化行為酶作為一類特殊蛋白質(zhì)與靶分子結(jié)合后會發(fā)生構(gòu)象變化降低化學(xué)反應(yīng)活化能、加快反應(yīng)速率且酶本身不會被消耗。在輔因子與環(huán)境信號共同作用下酶促進底物轉(zhuǎn)化的自適應(yīng)機制構(gòu)成了EAO算法的設(shè)計基礎(chǔ)。1.算法原理1.1 初始化和其他群優(yōu)化算法一樣采用隨機初始化X i ( 0 ) L B ( U B ? L B ) ⊙ r i , \mathbf{X}_i^{(0)} \mathbf{LB} (\mathbf{UB}-\mathbf{LB}) \odot \mathbf{r}_i,Xi(0)?LB(UB?LB)⊙ri?,X n [ x 1 , 1 x 1 , 2 … x 1 , D i m x 2 , 1 x 2 , 2 … x 2 , D i m ? ? ? ? x N , 1 x N , 2 … x N , D i m ] N × D i m X_{n} \begin{bmatrix} x_{1,1} x_{1,2} \dots x_{1,Dim} \\ x_{2,1} x_{2,2} \dots x_{2,Dim} \\ \vdots \vdots \ddots \vdots \\ x_{N,1} x_{N,2} \dots x_{N,Dim} \end{bmatrix}_{N \times Dim}Xn??x1,1?x2,1??xN,1??x1,2?x2,2??xN,2??……?…?x1,Dim?x2,Dim??xN,Dim???N×Dim?f b e s t min ? ( f ( X i ) ) f_{best} \min\left(f\left(X_i\right)\right)fbest?min(f(Xi?))X s u p e r i o r X ( arg ? min ? ( f ( X i ) ) ) X_{superior} X\left(\arg\min\left(f\left(X_i\right)\right)\right)Xsuperior?X(argmin(f(Xi?)))定義迭代t tt時刻的自適應(yīng)因子AFA F t t M a x I t e r . \mathrm{AF}_t\sqrt{\frac{t}{\mathrm{MaxIter}}}.AFt?MaxItert??.1.2 迭代階段每一次迭代t tt每個底物個體生成兩個候選位置。第一個底物候選位置更新公式X i , 1 ( t ) ( X b e s t ( t ? 1 ) ? X i ( t ? 1 ) ) ρ i ⊙ sin ? ( A F t ? X i ( t ? 1 ) ) , \mathbf{X}_{i,1}^{(t)} \left(\mathbf{X}_{\mathrm{best}}^{(t-1)}-\mathbf{X}_i^{(t-1)}\right) \boldsymbol{\rho}_i \odot \sin\left(\mathrm{AF}_t \cdot \mathbf{X}_\mathrm{i}^{(t-1)}\right),Xi,1(t)?(Xbest(t?1)??Xi(t?1)?)ρi?⊙sin(AFt??Xi(t?1)?),隨機選取兩個不同底物p pp、q qq計算二者距離向量d \mathbfbextvpldd X p ( t ? 1 ) ? X q ( t ? 1 ) , \mathbfbextvpl \mathbf{X}_p^{(t-1)}-\mathbf{X}_q^{(t-1)},dXp(t?1)??Xq(t?1)?,第二個底物候選位置更新公式X i , 2 ( t ) X i ( t ? 1 ) s c 1 d 1 A F t s c 2 ( X b e s t ( t ? 1 ) ? X i ( t ? 1 ) ) \mathbf{X}_{i,2}^{(t)} \mathbf{X}_i^{(t-1)} \mathrm{sc}_1 \mathbfbextvpl_1 \mathrm{AF}_t \mathrm{sc}_2 \left(\mathbf{X}_{\mathrm{best}}^{(t-1)}-\mathbf{X}_i^{(t-1)}\right)Xi,2(t)?Xi(t?1)?sc1?d1?AFt?sc2?(Xbest(t?1)??Xi(t?1)?)全局最優(yōu)個體更新規(guī)則i f F ( X i ( t ) ) F b e s t ( t ? 1 ) ? X b e s t ( t ) X i ( t ) , F b e s t ( t ) F ( X i ( t ) ) . \mathrm{if}\ F(\mathbf{X}_i^{(t)}) F_{\mathrm{best}}^{(t-1)} \implies \mathbf{X}_{\mathrm{best}}^{(t)}\mathbf{X}_i^{(t)},\ F_{\mathrm{best}}^{(t)}F(\mathbf{X}_i^{(t)}).ifF(Xi(t)?)Fbest(t?1)??Xbest(t)?Xi(t)?,Fbest(t)?F(Xi(t)?).2.實驗結(jié)果3.MatlabPython4.參考文獻[1] Rodan, A., Al?Tamimi, AK., Al?Alnemer, L. et al. Enzyme action optimizer: a novel bio?inspired optimization algorithm. J Supercomput 81, 686 (2025).