Research Article

Modified Biogeography-Based Optimization with Local Search Mechanism

Algorithm 4

Pseudocode of MLBBO.
(1) Begin
(2)  Initialize the population Pop with NP habitats randomly
(3)  Evaluate the fitness (HSI) for each Habitat in Pop
(4)  While the halting criteria are not satisfied do
(5)   Sort all the habitats from best to worst in line with their fitness values
(6)   Map the fitness to the number of species count S for each habitat
(7)   Calculate the immigration rate and emigration rate according to sinusoidal migration model
(8)   For   i = 1 to NP    /*migration stage*/
(9)    Select according to immigration rate using sinusoidal migration model
(10)   If   is selected
(11)    For   j = 1 to NP
(12)     Generate fourmutually different integers in
(13)     Select according to emigration rate
(14)     If   rand(0,1) <
(15)       
(16)     Else
(17)      
(18)     EndIf
(19)    EndFor
(20)   EndIf
(21)   EndFor                        /*end ofmigration stage*/
(22)  For   i = 1 to NP   /*mutation stage*/
(23)   Compute the mutation probability
(24)   Select variable with probability
(25)   If   is selected
(26)      
(27)   Endif
(28)  EndFor                            /*end ofmutation stage*/
(29)  For   i = 1 to NP/2  /*local search stage*/
(30)    Ifrand(0,1) <
(31)     Select a habitat randomly
(32)       *
(33)    EndIf
(34)  EndFor                        /*end oflocal search stage*/
(35)  Make sure each habitat legal based on boundary constraints
(36)  Use new generated habitat to replace duplicate
(37)  Evaluate the fitness (HSI) for trial habitat in the new population Pop
(38)  For   i = 1 to NP    /*selection stage*/
(39)    If
(40)       
(41)    EndIf
(42)  EndFor                        /*end ofselection stage*/
(43) EndWhile
(44) End