Model development for optimizing the base station site choice on the PMET-PSO algorithm

Abstract

Existing research in the field of mathematical modeling of base station planning, using intelligent optimization algorithms to solve potential schemes for generating base station distributions, is complex and, for the most part, mathematical models for choosing a base station site require simplification. Also, the existing models for choosing the base station site solve the problem in two-dimensional space and do not take into account the influence of terrain and other factors. Having incorporated the concept of membrane computing into the basis of the MET-PSO algorithm, the PMET-PSO algorithm was developed. The PMET-PSO algorithm is a redesign of the METROPOLIS sample in the simulated annealing algorithm, projects the probability of accepting METROPOLIS in accordance with the number of iterations of the particle swarm algorithm and determines whether to obtain an individual optimal position and a global optimal position generated by a new round of iterations in accordance with probability, thereby adding randomness to the particle swarm algorithm. As the number of iterations increases, the randomness of the particle swarm algorithm decreases, and it converges to an optimal solution. PMET-PSO allows parallel computing, which effectively reduces the time complexity of the MET-PSO algorithm.

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