Research Article

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

Table 2

Summary of the CEC′14 test functions.

TypeNo.FunctionsFi = Fi (x)

Unimodal functionsF1Rotated High-Conditioned Elliptic Function100
F2Rotated Bent Cigar Function200
F3Rotated Discus Function300

Simple multimodal functionsF4Shifted and Rotated Rosenbrock’s Function400
F5Shifted and Rotated Ackley’s Function500
F6Shifted and Rotated Weierstrass Function600
F7Shifted and Rotated Griewank’s Function700
F8Shifted Rastrigin’s Function800
F9Shifted and Rotated Rastrigin’s Function900
F10Shifted Schwefel’s Function1000
F11Shifted and Rotated Schwefel’s Function1100
F12Shifted and Rotated Katsuura Function1200
F13Shifted and Rotated HappyCat function1300
F14Shifted and Rotated HGBat Function1400
F15Shifted and Rotated Expanded Griewank’s Plus Rosenbrock’s Function1500
F16Shifted and Rotated Expanded Scaffer’s F6 Function1600

Hybrid functionsF17Hybrid function 1 (N = 3)1700
F18Hybrid function 2 (N = 3)1800
F19Hybrid function 3 (N = 4)1900
F20Hybrid function 4 (N = 4)2000
F21Hybrid function 5 (N = 5)2100
F22Hybrid function 6 (N = 5)2200

Composition functionsF23Composition function 1 (N = 5)2300
F24Composition function 2 (N = 3)2400
F25Composition function 3 (N = 3)2500
F26Composition function 4 (N = 5)2600
F27Composition function 5 (N = 5)2700
F28Composition function 6 (N = 5)2800
F29Composition function 7 (N = 3)2900
F30Composition function 8 (N = 3)3000

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