[Retracted] The Impact of IoT on News Media in the Smart Age

Algorithm 2

Hybridized fruit fly bumblebee optimization algorithm (HFBOA).

	Step 1: When entailing a community of bumblebees and computing their fitness function, initialize all variables, including the group size (m), maximum iterations, and the beginning placements of the fruit fly group (Ainitial, Binitial)
	Step 2: Using osphresis, each fruit fly will be allocated an unpredictable position and direction to obtain food, and the queen will select the drones for mating
	where j ranges from 1 to m and m is the magnitude of the fruit fly class
	Step 3: Calculate the distance from the source of food for each fruit fly, which is represented as (R)
	Calculate the distance from the source of food for each fruit fly, which is represented as (R)
	Step 4: To calculate the aroma relative density of each fruit fly, the aroma intensity decision value (D) is substituted in the aroma intensity decision function
	Step 5: Among several fruit flies, the fruit fly with the greatest aroma concentration value is picked. Keep track of its worth and score
	The new queen feeding strategy is used to enhance brood solutions so that the fittest may become new queens. Everyone else will have to work. To carry out this strategy, the combinatorial neighborhood topology (CNT) is employed
	Step 6: Keep the best odor relative density and its locations, and the fruit fly cluster will fly to that ideal area using its searching visualization
	Step 7: Check to determine whether it met the stop requirements; if not, repeat steps 2–5. Step 6 is performed if the scent density value is larger than the aroma concentration value from the previous iteration