A high accuracy localization algorithm with DV-Hop and fruit fly optimization in anisotropic wireless networks

Jia, Wenxian; Gao, Qi; Liu, Menghan; Zhou, Jie

doi:10.1016/j.jksuci.2022.07.022

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…The differential evolution algorithm retains the population-based global search strategy [5] , adopts simple mutation operation and one-to-one competitive survival strategy, has strong global convergence ability and robustness, and is suitable for solving some problems using conventional mathematical programming methods [6] . Optimization problems in complex environments that cannot be solved [7] . Combining differential evolution (DE) algorithm with particle swarm optimization (PSO) algorithm is one of the current research hotspots [8] .…”

Section: Introductionmentioning

confidence: 99%

Differential adaptive SA-DEPSO algorithm based on particle swarm

Peng

Zhao

2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.

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