A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival

Algorithm 1

: Squirrel search algorithm.

	Begin:
	Step 1: Define the input criteria
	Step 2: Random positions for n number of floating squirrels using (1)
	Step 3: Calculate the fitness of each floating squirrel’s position
	Sort the positions of floating squirrels in increasing order based on fitness value
	Step 4: Announce floating squirrels on hickory normal trees, acorn trees, and nut tree
	At Random elect, some floating squirrels move from normal trees t hickory nut trees, and the rest will move facing acorn trees
	while (the stopping requirement is not met)
	For t = 1 to n1 (n1 = total floating squirrels coming towards hickory nut tree from acorn trees)
	if R1 ≥ Pdp
	else
	= a random location of search area
	end
	end
	For t = 1 to n2 (n2 = total floating squirrels on normal trees traveling in the direction of acorn trees)
	if R2 ≥ Pdp
	else
	= a random location of search area
	end
	end
	For t = 1 to n3 (n3 = total floating squirrels on normal trees traveling in the direction of the hickory nut tree)
	if R3 ≥ Pdp
	Else
	= a random location of search area
	end
	end
	Step 5: Evaluate seasonal constant (Sc) using (7)
	if (condition for Seasonal monitoring is met)
	Randomly repositioned floating squirrels
	end
	Step 6: Update the lowest value of the seasonal constant
	End
	The position of a squirrel on the hickory tree is the concluding best solution
	End