A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization
Algorithm 2
Tuning of the optimal NNFOPID parameters for DDMR using EFFO.
(1)
Initialization phase.
Assign random initializations with upper and lower limits and initialize other algorithm parameters.
where is the initial value of the fruit fly position in the x-direction and and are the lower and upper limits of -position, respectively, [0, 1] is a random value, is the initial value of Y in the y-direction, and and are the upper and lower limits of , respectively. Define imax, . Define the number of fruit flies N, . Set iteration i = 1.
(2)
Osphresis searching process
Every individual searches about food in all directions randomly around the initial locations of the previous step using the osphresis organ to generate the next population.
For
For ,
While i < imax
For j = 1, 2, …, N
(3)
Path construction phase.
(4)
Fitness calculation phase.
Calculate the fitness function by the following equations, where (32) or (33) are calculated with the updated values of the NNFOPID parameters , i.e.,
End For
where is the vector of the concentration of smell for the fruit fly swarm, represents the smallest elements, are its indices along the different dimensions of smell vectors, and min is the minimum concentration of smell in the fly swarm.
(5)
Vision searching process
The fruit flies keep the best value of smell concentration and will use visionary sense to fly in the direction of that location according to the subsequent equations,
