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智能优化算法酶作用优化算法 Enzyme Action Optimizer (EAO)文章目录智能优化算法酶作用优化算法 Enzyme Action Optimizer (EAO)1.算法原理1.1 初始化1.2 迭代阶段2.实验结果3.MatlabPython4.参考文献酶作用优化算法EAO是模拟生物体内酶自适应催化机制的仿生元启发式优化算法。灵感来源于酶的催化行为酶作为一类特殊蛋白质与靶分子结合后会发生构象变化降低化学反应活化能、加快反应速率且酶本身不会被消耗。在辅因子与环境信号共同作用下酶促进底物转化的自适应机制构成了EAO算法的设计基础。1.算法原理1.1 初始化和其他群优化算法一样采用随机初始化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}Xnx1,1x2,1⋮xN,1x1,2x2,2⋮xN,2……⋱…x1,Dimx2,Dim⋮xN,DimN×Dimf b e s t min ( f ( X i ) ) f_{best} \min\left(f\left(X_i\right)\right)fbestmin(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)XsuperiorX(argmin(f(Xi)))定义迭代t tt时刻的自适应因子AFA F t t M a x I t e r . \mathrm{AF}_t\sqrt{\frac{t}{\mathrm{MaxIter}}}.AFtMaxItert.1.2 迭代阶段每一次迭代t tt每个底物个体生成两个候选位置。第一个底物候选位置更新公式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)),随机选取两个不同底物p pp、q qq计算二者距离向量d \mathbf{d}dd X p ( t − 1 ) − X q ( t − 1 ) , \mathbf{d} \mathbf{X}_p^{(t-1)}-\mathbf{X}_q^{(t-1)},dXp(t−1)−Xq(t−1),第二个底物候选位置更新公式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 \mathbf{d}_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)sc1d1AFtsc2(Xbest(t−1)−Xi(t−1))全局最优个体更新规则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.实验结果3.MatlabPython4.参考文献[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). 智能优化算法：酶作用优化算法 Enzyme Action Optimizer (EAO)
智能优化算法酶作用优化算法 Enzyme Action Optimizer (EAO)文章目录智能优化算法酶作用优化算法 Enzyme Action Optimizer (EAO)1.算法原理1.1 初始化1.2 迭代阶段2.实验结果3.MatlabPython4.参考文献酶作用优化算法EAO是模拟生物体内酶自适应催化机制的仿生元启发式优化算法。灵感来源于酶的催化行为酶作为一类特殊蛋白质与靶分子结合后会发生构象变化降低化学反应活化能、加快反应速率且酶本身不会被消耗。在辅因子与环境信号共同作用下酶促进底物转化的自适应机制构成了EAO算法的设计基础。1.算法原理1.1 初始化和其他群优化算法一样采用随机初始化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}Xnx1,1x2,1⋮xN,1x1,2x2,2⋮xN,2……⋱…x1,Dimx2,Dim⋮xN,DimN×Dimf b e s t min ( f ( X i ) ) f_{best} \min\left(f\left(X_i\right)\right)fbestmin(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)XsuperiorX(argmin(f(Xi)))定义迭代t tt时刻的自适应因子AFA F t t M a x I t e r . \mathrm{AF}_t\sqrt{\frac{t}{\mathrm{MaxIter}}}.AFtMaxItert.1.2 迭代阶段每一次迭代t tt每个底物个体生成两个候选位置。第一个底物候选位置更新公式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)),随机选取两个不同底物p pp、q qq计算二者距离向量d \mathbf{d}dd X p ( t − 1 ) − X q ( t − 1 ) , \mathbf{d} \mathbf{X}_p^{(t-1)}-\mathbf{X}_q^{(t-1)},dXp(t−1)−Xq(t−1),第二个底物候选位置更新公式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 \mathbf{d}_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)sc1d1AFtsc2(Xbest(t−1)−Xi(t−1))全局最优个体更新规则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.实验结果3.MatlabPython4.参考文献[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).