scholarly journals Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

2021 ◽  
pp. 242-249
Author(s):  
M.Shahkhir Mozamir ◽  
◽  
Rohani Binti Abu Bakar ◽  
Wan Isni Soffiah Wan Din ◽  
Zalili Binti Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Localization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

scholarly journals Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiobjective Discrete Binary Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhen-Lun Yang ◽  
Angus Wu ◽  
Hua-Qing Min
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Real Time ◽  
Monitoring System ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization

The deployment problem of wireless sensor networks for real time oilfield monitoring is studied. As a characteristic of oilfield monitoring system, all sensor nodes have to be installed on designated spots. For the energy efficiency, some relay nodes and sink nodes are deployed as a delivery subsystem. The major concern of the construction of the monitoring system is the optimum placement of data delivery subsystem to ensure the full connectivity of the sensor nodes while keeping the construction cost as low as possible, with least construction and maintenance complexity. Due to the complicated landform of oilfields, in general, it is rather difficult to satisfy these requirements simultaneously. The deployment problem is formulated as a constrained multiobjective optimization problem and solved through a novel scheme based on multiobjective discrete binary particle swarm optimization to produce optimal solutions from the minimum financial cost to the minimum complexity of construction and maintenance. Simulation results validated that comparing to the three existing state-of-the-art algorithms, that is, NSGA-II, JGGA, and SPEA2, the proposed scheme is superior in locating the Pareto-optimal front and maintaining the diversity of the solutions, thus providing superior candidate solutions for the design of real time monitoring systems in oilfields.

scholarly journals PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 343
Author(s):  
Chiao-Sheng Wang ◽  
Chen-Wei Conan Guo ◽  
Der-Min Tsay ◽  
Jau-Woei Perng
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Speed Control ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Estimation Equations ◽  
Policy Gradient

Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

2012 ◽  
pp. 291-304
Author(s):  
Hamid Ali ◽  
Waseem Shahzad ◽  
Farrukh Aslam Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Efficient ◽  
Efficient Solution ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Battery Power ◽  
Energy Efficient Clustering

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

An Energy Efficient Trust Based Routing Scheme Using Hybrid Particle Swarm Optimization for Wireless Sensor Based Healthcare Networks

2021 ◽  
Vol 11 (12) ◽  
pp. 3096-3102
Author(s):  
S. Gnana Selvan ◽  
I. Muthu Lakshmi
Keyword(s):  
Particle Swarm Optimization ◽  
Network Performance ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Second Phase ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

Healthcare networks are so sensitive and requires faster yet reliable data transmission. The problem based on congestion degrades the resources that lead to the failure of sensor nodes and faulty node misbehavior. In addition to this, increased energy computation, network performance minimizes the network lifetime. So to overcome such drawbacks, this paper proposes trust-based congestion aware using Hybrid Particle Swarm Optimization (HPSO) in Wireless Sensor based Healthcare Networks (WSHN). The proposed approach comprises two significant phases. The initial phase involves the calculation of congestion state among various nodes and the of trust values. Thus an optimal congestion metric is obtained. In the second phase, two diverse metrics namely distance and trust congestion metrics are executed using HPSO algorithm for optimal data packet routing from the base stations to the source node. This article presents a novel HPSO algorithm that utilises two distinct operators, namely the emigration and immigration processes, as well as the mutation process of the Bio-geographical based Optimization (BBO) algorithm, for presenting the optimal data routing protocol. The experimental outcomes and comparison analysis demonstrate that the proposed strategy outperforms several alternative approaches.

scholarly journals Sparse Based Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Lalit Kumar ◽  
Manish Pandey ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computation Time ◽  
Pso Algorithm ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Almost All

Abstract Particle Swarm Optimization (PSO) is the well-known metaheuristic algorithm for optimization, inspired from swarm of species.PSO can be used in various problems solving related to engineering and science inclusive of but not restricted to increase the heat transfer of systems, to diagnose the health problem using PSO based on microscopic imaging. One of the limitations with Standard-PSO and other swarm based algorithms is large computational time as position vectors are dense. In this study, a sparse initialization based PSO (Sparse-PSO) algorithm has been proposed. Comparison of proposed Sparse-PSO with Standard-PSO has been done through evaluation over several standard benchmark objective functions. Our proposed Sparse-PSO method takes less computation time and provides better solution for almost all benchmark objective functions as compared to Standard-PSO method.

scholarly journals A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

2021 ◽  
Vol 1 (2) ◽  
pp. 1-17
Author(s):  
Dan Zhang ◽  
Xiaohuan Zhang ◽  
Hai Qi
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Low Energy Consumption ◽  
Location Sensing ◽  
Range Free

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

scholarly journals Parallel energy-efficient coverage optimization using WSN with Image Compression

2013 ◽  
pp. 12-17
Author(s):  
Jibanananda Mishra ◽  
Ranjan Kumar Jena ◽  
Rabinarayana Parida ◽  
Abinash Panda
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Process Model ◽  
Sensor Node ◽  
Radial Basis Function Network ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrices are calculated by grid exclusion algorithm and Dijkstra’s algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combing the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.

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