Algorithm 2 The Improved Spider Wasp Optimization Algorithm
Input:	$N, Nmin, \mathrm{CR}, \mathrm{TR}, t_{max},lb,ub,dim,$
Output:	$\overrightarrow{{SW}^{*}}$
1.	Initialize parameters
2.	Set boundary conditions $lb,ub$
3.	Initialize $\overrightarrow{{SW}^{*}}$ as a zero vector
4.	Set optimal fitness to infinity
5.	Initialize positions of search agents
6.	$\mathrm{Use} \mathrm{initialization} \mathrm{function} \mathrm{to} \mathrm{generate} \mathrm{initial} \mathrm{positions} x_i$
7.	$\mathrm{Generate} \mathrm{an} \mathrm{opposite} \mathrm{population} \mathrm{using} \mathrm{opposition} \mathrm{learning} x_i^{\prime }$
8.	Combining the original and inverse populations
9.	$\mathrm{Evaluate} \mathrm{each} \overrightarrow{{SW}_i}$ $\mathrm{and} \mathrm{finding} \mathrm{the} \mathrm{one} \mathrm{with} \mathrm{the} \mathrm{best} \mathrm{fitness} \mathrm{in} \overrightarrow{{SW}^{*}}$
10.	$\mathrm{Main} \mathrm{loop} (t=0$ $\mathrm{to} t_{max}$)
11.	To calculate the values of TR, Cr, and k using Equations (26), (41) and (42),
12.	Randomly shuffle the index
13.	Judgement of hunting and nesting behavior based on TR
14.	$rand$ between 0 and 1
15.	If $rand<\mathrm{TR}$, perform hunting and nesting behavior
16.	$\mathrm{The} \mathrm{optimal} \mathrm{solution} \overrightarrow{{SW}^{*}}$ can be calculated using Formulas (4)–(8) from the exploration phase and Formulas (10), (12), (16) and (17) from the follow-escape phase.
17.	Perform boundary check
18.	Else, perform mating behavior (DE + COA hybrid strategy)
19.	Differential Evolution (DE) operates using Equations (38) and (39)
20.	Perform crossover operations
21.	Apply boundary check
22.	$\mathrm{The} \mathrm{optimal} \overrightarrow{{SW}^{*}}$ solution can be obtained by combining COA using Formulas (29)–(37), and then fusing the results of DE and COA, applying Formula (40).
23.	Apply opposition-based learning (OBL) every 10 iterations
24.	$\mathrm{Generate} \mathrm{an} \mathrm{opposite} \mathrm{population} \mathrm{using} \mathrm{opposition} \mathrm{learning} x_i^{\prime }$
25.	Combining the original and inverse populations
26.	if $f\left(U\right)<f\left(X\right)$
27.	$X=\overrightarrow{{SW}^{*}}$
28.	Else, U $=\overrightarrow{{SW}^{*}}$
29.	Reduce population size
30.	Compute the new population size ensuring it is not less than the minimum size
31.	Update the population accordingly