Minimization of Energy Consumption for Routing in High-Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization
Algorithm 1
Algorithm flow.
Improved adaptive elite ant colony optimization based on routing energy consumption
Begin
Step 1. Set the parameters and QoS constraints of each node and each edge in the model, and the relevant parameters in the algorithm are set. Set the pheromone volatilization coefficient, initialize pheromone value, and energy consumption revenue value. The upper limit of iterations is . The primary value is . The colony size is .
Set pheromone initial value of on the link ,.
While the algorithm has not reached the maximum number of iterations.
Step 2. Increase iteration times, .
Step 3. Increase the number of ants, .
Step 4. Calculate the selection probability of the next node , according to Equation (16).
Step 5. If ,continue step 4; otherwise, perform step 6.
Step 6. If , go to step 7; otherwise, go to step 3.
Step 7. Update pheromone adaptive adjustment, according to equation ((16), (22)).
Step 8. Find elite ants and update pheromone incrementally, according to Equation (9).
Step 9. When the condition meets the number of cycles , then output the result; otherwise, go to step 2.
