A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks

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.

Editorial

“…Several experiments are given to indicate that the performance of the proposed machine learning and deep learning methods could be better than that of the traditional machine learning methods. [61][62][63] In the future, the semantic web could be considered to represent the sensing data from distributed sensor networks. [64][65][66][67] For improving the performance of machine learning-based distributed sensor network applications, the advanced swarm intelligence techniques [68][69][70] could be applied.…”

“…The results show that the performance of the proposed QT-PIO was higher than that of QUATRE, PIO, and particle swarm optimization (PSO). 61…”

Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism

“…Therefore, a pre-defined Threshold Criterion tc has been adopted by exploring the Accepted Link Quality as given in Eq. 11, taking Triangle Matric (TM) [48], as a base point, a Link Corpus Table (lct) is being setup to maintain and improve the threshold for appropriate link factor. For this purpose, let consider pt as a absolute transmitted data packet and ps being the successful acknowledged packet by the destination node.…”

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

“…The larger the minimum hop-count value from the localization node to the beacon node, the greater the cumulative error value [ 27 ]. On account of the error is directly proportional to the hop-count value, when the hop-count value is large, the localization result will be inaccurate if the estimated distance with large error is used to calculate the node localization coordinates [ 28 , 29 ].…”