Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks
Algorithm 1
LGMRFO Algorithm.
Input: Initialize the size of population N, the maximal number of iterations T, and the manta rays X.
Output: The best solution Xbest.
(1)
Compute the fitness of each individual fi = f(Xi) and obtain the best solution found so far Xbest, where lb and ub are the lower and upper boundaries of problem space, respectively.
(2)
Initialize the iteration counter t = 0
(3)
While t < T
(4)
For i = 1 to N
(5)
if rand <0.5
(6)
if t/T < rand
(7)
Perform the exploratory behavior of cyclone foraging according to equation (10)
(8)
else
(9)
Perform the exploitative behavior of cyclone foraging according to equation (3)
(10)
end
(11)
else
(12)
Perform the chain foraging according to equation (1)
(13)
end
(14)
end
(15)
Greedy selection of the current individual
(16)
For i = 1 to N
(17)
Perform the adaptive t-distribution strategy according to equation (11)
(18)
end
(19)
Greedy selection of the current individual
(20)
For i = 1 to N
(21)
Perform the somersault foraging according to equation (6)
(22)
end
(23)
The population was divided into two groups according to the fitness value
(24)
Perform the group learning strategy according to equations (12) and (13)
(25)
Compute the fitness of each individual fi = f(Xi) and obtain the best solution found so far Xbest
