A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization

Algorithm 1

Tuning of the optimal NNFOPID parameters for DDMR using MAPSO.

(1)	Initialize the swarm in the range of about (6M–10M) particles.
(2)	Choose the best M swarm and randomly assign the initial location and velocities and other related parameters. Randomly assign the position values of PSO particles with zero velocities and initialize the constants , , , , . Set i = 1 and go to the step (3)
(3)	For j = 1, 2, …, M
	Calculate the fitness function of the particle j, i.e., using (32) or (33) to each particle j,
	set local best cost = current
	Local best position = current position
	End
	Set global best MSE = min (for all local best MSE)
(4)	,
	For
	For
	For j = 1, 2, …, M
	,
	Calculate the MSE of (32) or (33)
	End
	End
	End
(5)	Set i = i + 1 and go to step 4 until either iteration i reaches imax or convergence is achieved.