Identification of Control Parameters Using Taguchi Method for Hybrid Real-Binary Differential Evolution Algorithm and Its Applications in Electromagnetic Optimization

Table 7

Control parameters of each algorithm in Table 6.

Algorithms	Variable types	Control parameters
GA	Binary variables	Population size , crossover percentage , mutation percentage , mutation rate , roulette wheel selection, and selection pressure
BEO [13]		Population size , exploration ability control parameter , exploitation ability control parameter , and generation probability
HGWO [14]		Population size , coefficient is linearly decreased from 2 to 0 over the course of iterations, and inertia weight
HPSO [4]	Hybrid variables	Population size , inertia weight for binary part , the upper and lower boundaries of inertia weights for real part , , accelerating coefficients , and the maximum velocities for real part and binary part, ,
IHPSO [6]		All parameter sets are the same as the setting in HPSO
HDE [5]		Population size , the scaling factor , the crossover probabilities for real variables , and binary variables , mutation strategy: DE/best/1
		Population size , the upper and lower boundaries of scaling factors and , the crossover probabilities for real variables , and binary variables , mutation strategy: DE/rand/1
		Population size , the upper and lower boundaries of scaling factors and , the crossover probabilities for real variables , and binary variables , novel mutation strategy selection from DE/best/1 and DE/rand/1