Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

Algorithm 1

The proposed FOGJS.

	Input: The parameters of JS such as the distribution coefficient β, and the motion coefficient γ. People size N, dimension Dim, and maximum iterations Miter.
	Output: Optimal fitness value.
	Construct the initial value for the population through logistic chaotic map (Zi).
	While ( t < Miter) do
	Calculate the fitness function for an individual population.
	Choose the best location .
	Compute the time control c(t) using equation (6)
	For i = 1 to N do
	If c(t) ≥ 0.5 then
	Jellyfish follows ocean current
	The jellyfish position is updated by equation (11) with fractional-order modified.
	Else
	Jellyfish move inside a swarm
	If rand(0, 1) ≥ (1 − c (t)) then
	Jellyfish exhibits type passive motion.
	The jellyfish position is updated by equation (12) with fractional-order modified.
	Else
	Jellyfish exhibits type active motion.
	The jellyfish position is updated by equation (4).
	End if
	End if
	Check the boundary of the jellyfish location and calculate the new area.
	Update the location of the jellyfish and the most location
	Gaussian mutation mechanism
	If f (Z^new(t)) < f ((t + 1)) then
	update the optimal location to Z^new(t).
	End if
	End for
	t = t + 1
	End while