Binary Equilibrium Optimization Algorithm for Computing Connected Domination Metric Dimension Problem

Algorithm 1

Pseudo-BEOA.

	Initialize the particle’s populations i = 1,…,n
	Assign equilibrium candidates’ fitness a large number
	Assign free parameters a1 = 2; a2 = 1; GP = 0.5;
	While Iter < Max_iter
	For i = 1: number of particles (n)
	Calculate fitness of i^th particle
	If () < ()
	Replace with and fit () with ()
	Convert each into binary using S-shaped transfer function in Cbinary ij
	Calculate the fitness of each Cbinary ij
	Update new position of the candidate using (16)
	Else if () > ( ) & ()) < ()
	Replace with and fit () with ()
	Convert each into binary using S-shaped transfer function in Cbinary ij
	Calculate the fitness of each Cbinary ij
	Update new position of the candidate using (16)
	Else if () > (( ) & () > () & () < ()
	Replace with and fit () with ()
	Convert each into binary using S-shaped transfer function in Cbinaryij
	Calculate the fitness of each Cbinaryij
	Update new position of the candidate using (16)
	Else if () > () & () > () & () > () & ()) < ()
	Replace with and fit ( ) with ()
	Convert each into binary using S-shaped transfer function in Cbinary ij
	Calculate the fitness of each Cbinary ij
	Update new position of the candidate using (16)
	End (If)
	End (For)
	Construct the equilibrium pool
	Accomplish memory saving (if Iter >1)
	Assign using (12)
	For i = 1: number of particles (n)
	Randomly choose one candidate from the equilibrium pool (vector)
	Generate random vectors of λ⃗, r⃗ using (11)
	Construct using (11)
	Construct using (15)
	Construct using (14)
	Construct using (13)
	Update concentrations (1- F) using (16)
	End (For)
	Iter = Iter + 1
	End while
	Return candidate with best fitness value