Research Article
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 |
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