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The complexity of the MRGA is the same as that of RCGA because of having same operations used in MRGA except a difference between the operation of crossover and mutation .
In this section, we represent some numerical results obtained from the application of Evolutionary Algorithms such as DE, RGA, MRGA, ABC, and MABC.
The convergence graph has been shown in Figure 4 where MABC performs better towards the upcoming generations than the MRGA and all other application of Evolutionary Algorithms such as the standard ABC, RGA, and DE.
Figure 5 shows the number of active BSes with the standard ABC, RCGA, DE and our MRGA, and MABC.
Figure 6 shows a number of connected users towards a generation with MABC, MRGA, and the standard ABC, RCGA, and DE.
The performance of the transmission power consumption among the proposed MABC, MRGA, and traditional ABC, RCGA, and DE has been shown in Figure 7.
The MABC has successfully found much better configuration by comparing with the conventional DE and RCGA and even the modified RCGA (MRGA) in order to locate a proper location and also to adjust the range of the power along with their connected users.
Notations Parameters [P.sub.t]: Transmission power [C.sub.l]: Cable loss [A.sub.g]: Receiver antenna gain [N.sub.f]: Noise figure [L.sub.b]: Body loss [C.sub.f]: Carrier frequency G: Maximum number of generations [lambda]: Maximum number of iterations NU: Number of users F: Objective function [F.sub.i]: Fitness value of the ith solution SF: Scaling Factor for RCGA and MRGA [M.sub.r]: Mutation Rate for RCGA and MRGA BCR: Box Crossover Rate P: Population size [sigma](g): Mutation step size varying with current generation g w: Random inertia weight [phi]: Random number MNC: Maximum number of cycles [LG.sub.ulX]: Longitude (upper-left X) [LT.sub.ulY]: Latitude (upper-left Y) [LG.sub.lrX]: Longitude (lower-right X) [LT.sub.urY]: Latitude (lower-right Y).
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