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Subject: search.filters.subject.Particle swarm optimization (PSO)

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Now showing 1 - 10 of 49
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    Prediction of damage level of inner conventional rubble mound breakwater of tandem breakwater using swarm intelligence-based neural network (PSO-ANN) approach
    (Springer Verlag service@springer.de, 2019) Kuntoji, G.; Rao, S.; Rao, M.; Reddy, E.N.B.
    The conventional rubble mound breakwater is a coastal protective structure commonly used decades before which alone failed to withstand the deepwater wave and its energy, and suffered a catastrophic failure. Keeping in mind both the safe functioning of harbor and stability of the breakwater for the fast-growing economy of the country, different types of breakwaters are being developed to serve this purpose. Tandem breakwater is an innovative type of breakwater, which is a combination of main conventional rubble mound breakwater and submerged reef in front of it. One of the advantages of this breakwater is that most of the wave energy is dissipated and wave intensity is reduced by submerged reef and the smaller waves interact with main breakwater and ensure its stability. Experimental studies are laborious and time-consuming to conduct. Therefore, it is necessary to carry out the detailed study of tandem breakwater stability by making use of simple and alternate techniques using the experimental data. In the present study, an attempt is made to understand the suitability and applicability of PSO-ANN, a hybrid soft computing technique for predicting damage level of conventional rubble mound breakwater of tandem breakwater. Based on the experimental data available in Marine Structure Laboratory, NITK, Surathkal, India, soft computing models are developed. The performances of the models are evaluated using model performance indicators. Results obtained demonstrate that the proposed new approach can be used to predict the damage level of conventional rubble mound breakwater of tandem breakwater efficiently and accurately. © Springer Nature Singapore Pte Ltd. 2019
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    Swarm intelligence-based support vector machine (PSO-SVM) approach in the prediction of scour depth around the bridge pier
    (Springer Verlag service@springer.de, 2019) Marulasiddappa, B.M.; Rao, M.; Mandal, S.
    The mechanism of scour around the bridge pier is a complex phenomenon, and it is very difficult to make a common method to predict or estimate the depth of scour hole. In this paper, a hybrid model is developed, combining support vector machine and particle swarm optimization (PSO-SVM) to predict scour depth around a bridge pier. The input parameters such as sediment size (d50), the velocity of flow (U), and time (t) are used in the study to predict the scour depth. The models are developed with RBF, polynomial, and linear kernel functions, and the performances are evaluated using different statistical parameters such as CC, RMSE, NSE, and NMB. The predicted results are compared with measured scour depth. The predicted scour depth reveals that PSO-SVM with RBF kernel function model is found to be reliable and efficient in predicting the scour depth around bridge piers. © Springer Nature Singapore Pte Ltd. 2019
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    Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms
    (Institute of Physics Publishing helen.craven@iop.org, 2018) Shetty, R.P.; Sathyabhama, A.; Srinivasa Pai, P.
    The wind being a most promising renewable energy, has become a strong contender for fossil fuels. Optimizing the blade pitch angle of a wind turbine is important to obtain the maximum power output, as the other variables are considered to be uncontrollable. In this paper an effort has been made to compare performances of three different optimization algorithms namely Particle swarm optimization (PSO), Artificial bee colony (ABC) and cuckoo search (CS) for optimizing the blade pitch angle and hence optimize the power output of a 1.5 MW capacity, pitch regulated, three-bladed horizontal axis wind turbine operating at a large wind farm in central dry zone of Karnataka. The objective function development is done using Artificial Neural Network. The CS algorithm is found to be faster and more efficient as compared to ABC and PSO for the problem under consideration. © Published under licence by IOP Publishing Ltd.
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    Particle Swarm Optimization based Maximum Power Point Tracking Technique for Solar PV System under Partially Shaded conditions
    (Institute of Electrical and Electronics Engineers Inc., 2021) Naseem, M.; Husain, M.A.; Kumar, J.; Ahmad, M.W.; Minai, A.F.; Khan, A.A.
    To attain peak power from a PV source, the maximum power point tracking (MPPT) approach is frequently used. The MPP of a photovoltaic (PV) system is not constant since its output characteristics are dependent on numerous parameters. Partial shading causes significant changes in the PV system's attributes, and it often shows several local maxima as well as global maxima. Due to the development of local maxima, traditional MPPT methods fail in partial shade situations. Due to partial shade, solar systems frequently have hot spots, which not only disrupt system yield power but also compromise the system's dependability and safety. Due to the existence of many peaks in the P-V curve under partial shade, the traditional MPPT becomes stuck in local maxima rather than the global peak. As a result, sophisticated MPPT systems are required to accurately track the real peak power despite changing temperature and irradiation circumstances. To accomplish this, this study proposes a tracking scheme based on particle swarm optimization (PSO). The suggested MPPT is simple, versatile, precise, and economical, and it can track global MPP even when partially shaded. The proposed algorithm's performance is examined in MATLAB Simulink to test the effectiveness of the recommended MPPT technique. © 2021 IEEE.
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    Nonlinear system identification using memetic differential evolution trained neural networks
    (2011) Subudhi, B.; Jena, D.
    Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.
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    Pickup and delivery problem using metaheuristics techniques
    (2012) D'Souza, C.; Omkar, S.N.; Senthilnath, J.
    Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. © 2011 Published by Elsevier Ltd. All rights reserved.
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    Hierarchical clustering algorithm for land cover mapping using satellite images
    (2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Archana Shenoy, B.
    This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust. © 2012 IEEE.
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    MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures
    (2012) Omkar, S.N.; Venkatesh, A.; Mudigere, M.
    This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. © 2012 Elsevier Ltd. All rights reserved.
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    Multiobjective discrete particle swarm optimization for multisensor image alignment
    (2013) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karthikeyan, T.
    A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient. © 2004-2012 IEEE.
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    A review on machining of titanium based alloys using EDM and WEDM
    (Institute of Problems of Mechanical Engineering mpm@def.ipme.ru, 2014) Manjaiah, M.; Narendranath, S.; Basavarajappa, S.
    The aim of this review is to present the consolidated information about the contributions of various researchers on the application of EDM and WEDM on titanium materials and subsequently identify the research gaps. The literature survey has been carried out from three perspectives such as application of EDM and WEDM on titanium materials, utilization of tools and techniques for correlating experimental results and application of products produced by EDM and WEDM. Three main research areas has been identified. First, the application of EDM and WEDM on titanium materials mainly TiNi based alloys. Second, the utilization of advanced tools and techniques such as artificial neural network (ANN), advanced particle swarm optimization (PSO) and tabu enhanced genetic algorithm (GA). Third, the study and analysis of surface integrity in EDM and WEDM on titanium materials. In addition, the paper has also evolved the future research directions. The paper has been concluded by indicating the future research directions for the research gaps identified during this literature survey.