Convergence Analysis of a Dynamic Discrete PSO Algorithm

Luo, Guilan; Zhang, Hai; Song, Chunhe

doi:10.1109/icinis.2008.100

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…The better performance of PSO is due to the continuity of the problem of WSN deployment. Based on the research [ 91 , 92 ], the PSO algorithm is suitable for continuous optimization problems. WSN deployment is a continuous problem as the sensor nodes can be located at any position in the region.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Shakeri

Sadeghi‐Niaraki

Choi

et al. 2020

Sensors

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With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

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Section: Discussionmentioning

confidence: 99%

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Shakeri

Sadeghi‐Niaraki

Choi

et al. 2020

Sensors

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show abstract

“…To generate a new particle p i k+1 , the final swap sequence is applied on particle p i k mentioned in above equation (1). From [14], it can be found that the convergence condition for this PSO is given by,…”

Section: Discrete Particle Swarm Optimizationmentioning

confidence: 99%

Torus Topology based Fault-Tolerant Network-on-Chip Design with Flexible Spare Core Placement

Bhanu

Kulkarni

Soumya

et al. 2018

2018 14th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)

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The increase in the density of the IP cores being fabricated on a chip poses on-chip communication challenges and heat dissipation. To overcome these issues, Network-on-Chip (NoC) based communication architecture is introduced. In the nanoscale era NoCs are prone to faults which results in performance degradation and un-reliability. Hence efficient fault-tolerant methods are required to make the system reliable in contrast to diverse component failures. This paper presents a flexible spare core placement in torus topology based faulttolerant NoC design. The communications related to the failed core is taken care by selecting the best position for a spare core in the torus network. By considering this we propose a metaheuristic based Particle Swarm Optimization (PSO) technique to find suitable position for the spare core that minimizes the communication cost. We have experimented with several application benchmarks reported in the literature by varying the network size and by varying the fault-percentage in the network. The results show significant reduction in terms of communication cost compared to other approaches reported in literature.

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A Discrete Particle Swarm Optimization Technique for Designing a 3D NoC-Based Multi-Core Systems

Manna

Mathew

2019

Design and Test Strategies for 2d/3d Integration for NoC-based Multicore Architectures

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Convergence Analysis of a Dynamic Discrete PSO Algorithm

Cited by 14 publications

References 6 publications

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Torus Topology based Fault-Tolerant Network-on-Chip Design with Flexible Spare Core Placement

A Discrete Particle Swarm Optimization Technique for Designing a 3D NoC-Based Multi-Core Systems

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