Algorithm 1Parameter Optimization Algorithm.

Parameter Initialization:

$C_r=[{\gamma }_{start}=0,h_{start}=0.001,K_{start}=0.001;{\gamma }_{end}=1,h_{end}=1,K_{end}=200]$: the searching intervals;

$N_c=3$: number of chromosome;

$N_p=100$: Number of individuals in the population;

$\chi =0.95$: The fraction to be replaced by crossover in each iteration;

$\mu =0.01$: The mutation rate;

$M=10$: The maximal iteration times;

${\lambda }_{stop}=0$: The threshold of stop condition.

Initialize generation 0:

k:=0;

$P_k$:=a population of $N_p$ randomly-generated individuals;

Evaluate $P_k$:

Compute fitness criteria SNRI for each $i\in P_k$;

{

1: Compute the corresponded MSR output by fourth order Runge–Kutta (RK4) method according to    Equation (6) and obtain $x\left[n\right](n=1,2,\dots ,N)$, where N is the length of the time series;

2: Compute the SNRI according to Equation (14) and Equation (15);

} Create generation k+1:

do

{

1: Copy: Select $(1-\chi )\ast n$ members of $P_k$ and insert into $P_{k+1}$;

2: Crossover: Select $\chi \ast n$ members, pair them up to produce offspring and insert the offspring into $P_{k+1}$;

3: Mutate: Select $\mu \ast n$ members of $P_{k+1}$, and invert a randomly selected bit;

4: Evaluate $P_{k+1}$;

5: if $P_{k+1}-P_k\le {\lambda }_{stop}$ then break;

6: else

7:   Increment: k:=k+1;

8: end if

}

while $k⩽M$;

return the optimal fittest individual from $P_M$;