QDEA

AcronymDefinition
QDEAQueensland Department of Education and the Arts (Australia)
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Step 2: For each solution in the population S, we evolve each solution according to the QDEA relay selection scheme, then we formulate H new solutions.
Step 3: Formulate another H new solutions according to another objective function through the QDEA. These solutions are also put into the population S.
Step 5: For the population [S.sub.2], evolve each quantum individual by using one object(MAR, MPF or MMR) as the evolutionary direction according to the QDEA.
Step 6: For the population [S.sub.3], evolve each quantum individual by using another object (MAR, MPF or MMR) as the evolutionary direction according to the QDEA.
5.1 Simulations based on QDEA for single-objective relay selection scheme
The used relay selection methods are as follows: quantum bee colony optimization (QBCO) based optimal relay selection, differential evolutionary algorithm (DEA) based optimal relay selection, artificial bee colony (ABC) based optimal relay selection, and the proposed quantum differential evolutionary algorithm (QDEA).
For QBCO, DEA, ABC and QDEA, the maximal iteration is set as 1000 and the population size H is set as 20.
4 consider the case where the MPF or MMR varies respectively with the iteration number for QBCO, DEA, ABC and QDEA when N=10, M=20, the SN power is 20W and the RN power is 18W.
The proposed QDEA applies the quantum computing theory to the differential evolution algorithm.
7, it can also be seen that ABC performs worse than QDEA, QBCO and DEA, while QBCO performs better than DEA and ABC, but QDEA performs the best.