Research Article
A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs
Algorithm 1
Clonal expansion and high-probability mutation.
Input: The population sorted according to the energy consumption value from small to large is set to pop1, the number of individuals is pop_num, | Output: The cloned population is set to pop2 | 1. First perform multi-level cloning operations | 2. for p ∈ [1, pop_num] | 3. if p<pop_num×0.2 | 4. Assign the individual of pop1(1) to pop2(p) | 5. else if p <pop_num×0.5 | 6. Assign the individual of pop1(2) to pop2(p) | 7. else if p <pop_num×0.7 | 8. Assign the individual of pop1(3) to pop2(p) | 9. else | 10. Assign the individual of pop1(4) to pop2(p) | 11. end if | 12. end for | 13. Then perform high-probability mutation operations | 14. for p ∈ [1, pop_num] | 15. r is a random number and r∈ [0,1] | 16. if r<0.3 | 17. Perform mutation operation on pop2(p) | 18. end if | 19. end for | 20. Return pop_2 |
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