COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment

Algorithm 1

CAWOA algorithm flow.

Using the dynamic nonlinear characteristics to improve the convergence accuracy and speed of the WOA, the flowchart of the CAWOA is as follows:
	The maximum iteration is Tmax, the population number is N, and N initial whale populations {Xi, i = 1, 2,,N} are generated.
	The fitness value { f (Xi), i = 1, 2,,N } of each whale individual is calculated, and the best individuals are recorded.
	While (t < Tmax) do
	for i = 1 to N do
	According to formula (10), the value of adaptive inertia weight W is calculated.
	According to formula (4), the value of control parameter alpha is calculated.
	Updating the values of other parameters A, C, l, and P.
	If () do
	According to formula (11), updating the current whale individual position;
	Else if () do
	According to formula (11), updating the current whale individual position;
	End if
	End for
	Calculating the fitness values of individuals in groups { f (Xi), i = 1, 2,,N}, and preserving and recording elite individuals.
	Using sin chaotic search strategy to update elite individuals;
	T = t + 1;
	end while t = t + 1;