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Abstract
In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-Pigeon-Inspired Optimization Algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary Algorithm for the Pigeon-Inspired Optimization Algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the Pigeon-Inspired Optimization algorithm and makes the Pigeon-Inspired Optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-Pigeon-Inspired Optimization algorithm into the Distance Vector–Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location.
Highlights
The optimization problem is the values of a set of parameters under certain constraint conditions, so as to obtain a certain performance, and play a role to a certain extent
For the sake of more intuitively evaluating the quality of the QTPIO algorithm, Figure 1 exhibits the curves of Pigeon-Inspired Optimization (PIO), QUasiAffine TRansformation Evolutionary (QUATRE), particle swarm optimization (PSO), QT-PIO with the best final fitness value obtained minimum for the unimodal functions
To improve the capability and performance of PIO, this study proposed a new optimization algorithm, which mainly applies the co-evolutionary M and mutation B in QUATRE algorithm to PIO algorithm, and enables individuals to update their positions in the form of matrix in the process of optimization
Summary
The optimization problem is the values of a set of parameters under certain constraint conditions, so as to obtain a certain performance, and play a role to a certain extent. More and more intelligence algorithms are used to optimize DV-Hop, this paper mainly takes advantage of QT-PIO to optimize that decreases the error of positioning, makes the location more accurate, and ensures a certain degree of accuracy. This text mainly proposes a new approach with regard to finding an optimal solution. The optimization ability performs well and effectively improves the convergence speed in this model Applying it to the DVHop algorithm and the experimental results indicate that our consideration greatly reduces errors of positioning and improves positioning accuracy.
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