A hierarchical hybrid of genetic algorithm and particle swarm optimization for distributed clustering in large-scale wireless sensor networks

Su, Shengchao; Zhao, Shenghui

doi:10.1007/s12652-017-0619-9

Cited by 20 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The chosen approach is based on the work of Shengchao et al who present a hybrid optimization procedure for clustering large‐scale wireless sensor networks 40 . The hybrid method combines Genetic Algorithms (GA) and Particle‐Swarm Optimization (PSO).…”

Section: Methodsmentioning

confidence: 99%

“…The chosen approach is based on the work of Shengchao et al who present a hybrid optimization procedure for clustering large-scale wireless sensor networks. 40 The hybrid method combines Genetic Algorithms (GA) and Particle-Swarm Optimization (PSO). Both methods are already used in the context of parameter identification of material models and have specific advantages.…”

Section: Parameter Identification Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimization workflow to parameterize elastomer material models based on arbitrary time‐domain measurement data

Rapp

Jacobs

Berroth

et al. 2023

J of Applied Polymer Sci

View full text Add to dashboard Cite

Predicting the behavior of elastomer components always requires experimental characterization of their nonlinear mechanical properties. This involves measuring the ratio of stresses and strains and the dissipated energy per load cycle in harmonic deformations of test specimens. Characteristic values, such as the elastomer's stiffness and damping, and their dependence on amplitude and frequency are then calculated and used to identify material model parameters.However, determining these characteristic values imposes significant requirements on test setup and control, as they are only meaningful when calculated from ideally sinusoidal and steady-state load sequences. This can result in unnecessary methodological errors for any real-world measurement. This contribution presents a method for identifying material model parameters that omits determining characteristic values, greatly reducing testing times and prediction errors. The method involves an optimization-based variation of model parameters to directly match the stress response of a material model with measured stresses. Its independence from ideal test sequences is shown by applying the method to numerically generated measurement data with artificial control errors. Application to real measurement data reveals a possible reduction of testing times from about 2 h to below 35 s while preserving the model's accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Parameter Identification Methodsmentioning

confidence: 99%

Optimization workflow to parameterize elastomer material models based on arbitrary time‐domain measurement data

Rapp

Jacobs

Berroth

et al. 2023

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

“…Liang and Suganthan [17] provided a dynamic multiswarm PSO by regrouping the swarms during the process to achieve a higher diversity. In addition, there are many research works about the distributed setting [18][19][20] to ensure a high parallelism and good performance. Zhao et al [21] improved the dynamic multiswarm PSO by hybridizing it with harmony search and developed a new optimizer.…”

Section: Related Workmentioning

confidence: 99%

Coverage control optimization algorithm for wireless sensor networks based on combinatorial mathematics

Wang¹

2018

Discrete &Amp; Continuous Dynamical Systems - S

View full text Add to dashboard Cite

e traditional wireless sensor network coverage control optimization algorithm has the problems of long completion time, high energy consumption, and low coverage. A new algorithm based on combinational mathematics for wireless sensor network coverage control is proposed. e particle swarm optimization (PSO) algorithm is used to optimize the coverage control process of wireless sensor networks. en, the combined mathematics method is used to detect the local convergence problem. Finally, the quasi-physical forces of quasi-gravity and Coulomb force are used to integrate the quasi-physical force into the particle. In the process of velocity evolution, the speed correction process of particle swarm optimization is optimized, which can effectively avoid the local convergence problem of the particle swarm optimization algorithm, reduce the repeated coverage, and expand the coverage.e experimental results show that compared with the traditional algorithm, the proposed algorithm has short completion time, low energy consumption, and high coverage.

show abstract

“…This strategy eliminates redundancies by reducing and gathering information. For capturing information from multiple WSNs, dynamic clustering is the best option [9]. As a result of some aspects, such as energy-saving capability and efficient scalability, dynamic clustering has attracted the interest of various academics.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks

Adumbabu

Selvakumar

2022

Energies

View full text Add to dashboard Cite

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

show abstract

A hierarchical hybrid of genetic algorithm and particle swarm optimization for distributed clustering in large-scale wireless sensor networks

Cited by 20 publications

References 31 publications

Optimization workflow to parameterize elastomer material models based on arbitrary time‐domain measurement data

Optimization workflow to parameterize elastomer material models based on arbitrary time‐domain measurement data

Coverage control optimization algorithm for wireless sensor networks based on combinatorial mathematics

Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks

Contact Info

Product

Resources

About