• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.650-655
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• 2012

This paper proposed PSO-Fuzzy controller design method. We could improve the learning performance of fuzzy controller by using PSO algorithm, which had recently showed its robust of performance while solving various difficult optimization problems. In other words, our aim was to forward the controller is performance by deciding fuzzy model structure that had good performance on optimization of the controller, based on PSO. During a simulation, we could see whether the mobile robot could convergence on the final goal or not, and also see the error, and through this process, we found out that this controller is more robust than the conventional controller.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.464-471
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• 2013

This paper proposes a hybrid searching algorithm based on parameter identification for power system load models. Hybrid searching was performed by the combination of particle swarm optimization (PSO) and a complex method, which enhances the convergence of solutions closer to minima and takes advantage of global searching with PSO. In this paper, the load model of interest is composed of a ZIP model and a third-order model for induction motors for stability analysis, and parameter sets are obtained that best-fit the output measurement data using the hybrid search. The origin of the hybrid method is to further apply the complex method as a local search for finding better solutions using the selected particles from the performed PSO procedure.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.328-338
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• 2006

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.673-686
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• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.