Power Transmission Network Optimization Strategy Based on Random Fractal Beetle Antenna Algorithm

Table 1

Parameter settings.

Algorithm	Detail
FPA	Population size: 50
	Iterations: 1000
	Search range/limits: [−5, 5] for each variable
	Fitness function: mean squared error
	Mutation rate: 0.2
	Termination criterion: 6000
GWO	Population size: 30
	Iterations: 500
	Search range/limits: [−10, 10] for each variable
	Fitness function: Rosenbrock function
	Mutation rate: 0.1
	Termination criterion: 5000
WOA	Population size: 50
	Iterations: 1000
	Search range/limits: [−100, 100] for each variable
	Fitness function: sphere function
	Mutation rate: 0.5
	Termination criterion: 6000
KH	Population size: 100
	Iterations: 2000
	Search range/limits: [−50, 50] for each variable
	Fitness function: Ackley function
	Mutation rate: 0.2
	Termination criterion: 7000
PSO-TVAC	Population size: 40
	Iterations: 800
	Search range/limits: [−1, 1] for each variable
	Fitness function: Rastrigin function
	Termination criterion: 6000
BAS	Population size: 50
	Iterations: 1000
	Search range/limits: [−5, 5] for each variable
	Fitness function: Griewank function
	Mutation rate: 0.2
	Termination criterion: 8000
BAS-RDEO	Mutation rate: 0.3
	Radius: 0.1
	Encircling rate: 0.5
	Termination criterion: 6000