Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Table 1

Description of parameters.

Parameters	Description
Number of individuals	An individual represents a solution to a low-energy clustering problem
Number of iterations	Algorithm optimization times
Mutation rate	Probability of binary code mutation
Crossover rate	Probability of binary change exchange between two individuals
Learning factors C1 and C2	Acceleration constant, normally, C1 = C2 = 2
Maximum velocity of the particle	Maximum speed of particle movement
The initial temperature	A sufficiently large temperature defined before the first iteration
The annealing temperature coefficient	Cooling rate coefficient, when the cooling rate coefficient is smaller, the cooling rate is faster