Improving particle swarm optimization: Using neighbor heuristic and Gaussian cloud learning

Donghui, Zhan; Lu, Houqing; Hao, Wenning; Jin, Dawei

doi:10.3233/ida-150799

Cited by 10 publications

(9 citation statements)

References 33 publications

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“…Here random compressive sensing using particle swarm intelligence will be used because of its agnostic and universal nature. Previous investigations on CS showed that signals that can be compressed using classical deterministic methods can also be efficiently acquired from a small set of random measurements.…”

Section: Methodsmentioning

confidence: 99%

Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics

Yao

Pakzad

Venkitasubramaniam

2016

Struct. Control Health Monit.

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Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network is a computationally expensive BIG DATA problem; therefore scalable algorithms are needed so that inferences about the current state of the structure can be made efficiently. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization is proposed and investigated. At each iteration, damage existence is identified from randomly collected sparse samples and damage localization is iteratively detected via sensing-processing cycles with metaheuristic sampling distribution updating. Specifically, simulated annealing and ant colony analogy are used for guidance in future selection of sensing locations. This framework is subsequently validated by numerical and experimental implementations for gusset plate crack identification. SHM systems [25,26], the transmission cost and energy consumption of centralized data processing becomes inhibitive as the sensor network expands in size [3,[27][28][29]. To overcome this obstacle, decentralized information extraction techniques and communication procedures have been proposed and tried in several studies [30][31][32][33], demonstrating increased energy efficiency and better flow of operation.In the meantime, options to compress the data for wireless transmission are also explored. This can be done by applying the off-the-shelf compression schemes via adaptive coding, and a number of studies that exploit the temporal information redundancy of structural monitoring signals for data condensation are also under way. Examples include compressive sensing from random selection [34][35][36], sparse matrix representation[37-39], bio-mimicry based signal interpretation [40], and wavelet analysis [41,42]. Such methods reduce the amount of data transmitted per node, yet the central station still needs to communicate directly/indirectly with all sensor nodes within network. But because damage is intrinsically a local phenomenon, even higher performance could be realized if within a dense network only a subset of sensors in the vicinity of possible damage communicates their data with the main repository given proper damage detection features and effective compressive data communication procedures.In this paper an iterative compact sensing framework on subset identification within dense sensor network for damage detection is introduced. It complements the existing temporal compressive sensing methods, where time histories are compressed at individual sensor nodes. In this scheme, damage existence is first identified via hypothesis testing on the determinant of sample correlation matrix, as a damage feature, among several random locations. Then damage is localized as the location where the maximum Damage Location Indicator (DLI) from iterative random sampling occurs. At each iteration damage indices are computed from the n...

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Section: Methodsmentioning

confidence: 99%

Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics

Yao

Pakzad

Venkitasubramaniam

2016

Struct. Control Health Monit.

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“…Therefore, the Gaussian mutation has a strong local search ability and poor global search ability. In addition, there are many variants of the Gaussian strategy, such as Zhan and Lu’s neighbor heuristic and Gaussian cloud learning particle swarm optimization algorithm [ 55 ].…”

Section: Mutation Strategy and Inertia Weight Strategy Of Psomentioning

confidence: 99%

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Zhang

Chen

et al. 2018

Open Life Sciences

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With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.

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“…Therefore, scholars have proposed many improvement strategies for a much efficient and effective PSO [34][35][36][37]. Recently, Zhan et al [38] proposed an improved PSO based on neighbor heuristic and Gaussian cloud learning, and the results proved its superiority over many PSO variants. We also adopt a Gaussian cloud operator combined with an adaptive parameter strategy and a Restart strategy to improve the general PSO algorithm of the twostage SCLAP.…”

Section: Design Of the Ipsomentioning

confidence: 99%

“…The membership cloud was first proposed by Deyi et al [39], a member of the Chinese Academy of Sciences. The method bridges the gap between quantitative methodology and qualitative methodology based on the fuzzy set theory and has been successfully applied in algorithm improvement [38,40].…”

Section: Gaussian Cloud Operator the Gaussian Cloud Operatormentioning

confidence: 99%

Solving a Two‐Stage Stochastic Capacitated Location‐Allocation Problem with an Improved PSO in Emergency Logistics

Deng¹,

Zhu²,

Tang³

et al. 2017

Mathematical Problems in Engineering

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A stochastic expected value model and its deterministic conversion are developed to formulate a two-stage stochastic capacitated location-allocation (LA) problem in emergency logistics; that is, the number and capacities of supply centers are both decision variables. To solve these models, an improved particle swarm optimization algorithm with the Gaussian cloud operator, the Restart strategy, and the adaptive parameter strategy is developed. The algorithm is integrated with the interior point method to solve the second-stage model. The numerical example proves the effectiveness and efficiency of the conversion method for the stochastic model and the proposed strategies that improve the algorithm.

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Improving particle swarm optimization: Using neighbor heuristic and Gaussian cloud learning

Cited by 10 publications

References 33 publications

Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics

Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Solving a Two‐Stage Stochastic Capacitated Location‐Allocation Problem with an Improved PSO in Emergency Logistics

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