Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Algorithm

1: DMSDL-QBSA.

Variable setting: number of iterations:, bird positions: , local optimum: , global optimal position:, global optimum:,and fitness of sub-swarm optimal position: ;
Input: population size: , dimension’s size: , number of function evaluations:, the time interval of each bird flight behavior: , the probability of foraging behavior: , constant parameter: , , , , , , and contraction expansion factor: ;
Output: global optimal position: , fitness of global optimal position: ;
(1)	Begin
(2)	Initialize the positions of N birds using equations (16)-(17): ;
(3)	Calculated fitness: ; set to be and find ;
(4)	Whiledo
(5)	/∗ Dynamic sub-swarm∗/
(6)	Regroup and of the sub-swarms randomly;
(7)	Sort the and refine the first best ;
(8)	update the corresponding ;
(9)	/∗Differential learning scheme∗/
(10)	For
(11)	Construct using DMS-BSA
(12)	Differential evolution: construct using equations (9)-(10);
(13)	Crossover: construct using equation (11);
(14)	Selection: construct using equation (12);
(15)	End For
(16)	/∗Birds position adjusting∗/
(17)	If
(18)	For
(19)	If
(20)	Foraging behavior: update the position of birds using equation (8);
(21)	Else
(22)	Vigilance behavior: update the position of birds using equation (2);
(23)	End If
(24)	End For
(25)	Else
(26)	Flight behavior is divided into producers and scroungers;
(27)	For
(28)	If is a producer
(29)	Producers: update the position of birds using equation (5);
(30)	Else
(31)	Scroungers: update the position of birds using equation (6);
(32)	End If
(33)	End For
(34)	End If
(35)	Evaluate ;
(36)	Update and ;
(37)	;
(38)	End While
(39)	End