Chaotic Elite Clone Evolutionary Algorithm Based Target Scheduling Method for High Density Wireless Sensor Networks

2017 ◽  
Author(s):  
Jie Zhou ◽  
Min Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
High Density ◽  
Wireless Sensor ◽  
Scheduling Method

scholarly journals Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammadjavad Abbasi ◽  
Muhammad Shafie Bin Abd Latiff ◽  
Hassan Chizari
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Sensor Node ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Network Coverage ◽  
Detection Model

Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.

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

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093274 ◽  
Author(s):  
Xiao-Xue Sun ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Pei Hu ◽  
Ai-Qing Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Affine Transformation ◽  
Optimal Solution ◽  
Wireless Sensor ◽  
Local Optimum ◽  
Search Range ◽  
Homing Behavior

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.

scholarly journals A Self-organizing Wireless Sensor Networks Based on Quantum Ant Colony Evolutionary Algorithm

2017 ◽  
Vol 13 (07) ◽  
pp. 69 ◽  
Author(s):  
Lin-lin Wang ◽  
Chengliang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Dynamic Adjustment ◽  
Coverage Problem ◽  
Self Organizing

<p><span style="font-size: medium;"><span style="font-family: 宋体;">Aiming at the coverage problem of self-organizing wireless sensor networks, a target coverage method for wireless sensor networks based on Quantum Ant Colony Evolutionary Algorithm (QACEA) is put forward. This method introduces quantum state vector into the coding of ant colony algorithm, and realizes the dynamic adjustment of ant colony through quantum rotation port. The simulation results show that the quantum ant colony evolutionary algorithm proposed in this paper can effectively improve the target coverage of wireless sensor networks, and has obvious advantages compared with the other two methods in detecting the number of targets and the convergence speed. Based on the above findings, it is concluded that the algorithm proposed plays an essential role in the improvement of target coverage and it can be widely used in the similar fields, which has great and significant practical value.</span></span></p>

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