Research Article
On the Theoretical Analysis of the Plant Propagation Algorithms
Algorithm 1
PPA for constrained optimisation [
14].
(1) Counter for trial runs; Population size | (2) | (3) Population of runners | (4) | (5) for : 100 do | (6) | (7)if then | (8) | (9)Create a random population of plants pop , and | gather the best solution from each run. | (10) | (11)end if | (12) | (13)while do | (14) | (15)Use population formed by gathering all the best solutions of previous runs. | Calculate value for each column of . | (16) | (17)end while | (18) | (19)Evaluate the population . | (20) | (21)Assume number of runners to be , | (22) | (23)while (the stopping criteria is not satisfied) do | (24) | (25)for to do | (26) | (27)for to do | (28) | (29)if then | (30) | (31)if rand then | (32) | (33)Generate a new solution according to Equation (1a); | (34) | (35)Evaluate it and store it in ; | (36) | (37)end if | (38) | (39)if rand then | (40) | (41)Generate a new solution according to Equation (1b); | (42) | (43)Evaluate it and store it in ; | (44) | (45)end if | (46) | (47)else | (48) | (49)for do | (50) | (51)if () or (rand ) then | (52) | (53) Update the th entry of , by using Equation (1c); | (54) | (55)end if | (56) | (57)Evaluate new solution and store it in ; | (58) | (59)end for | (60) | (61)end if | (62) | (63)end for | (64) | (65)end for | (66) | (67)Append to current population; | (68) | (69)Sort the population in ascending order of objective values; | (70) | (71)Update current best; | (72) | (73)end while | (74) | (75)Return: Updated population and global best solution. | (76) | (77) end for | (78) |
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