597 research outputs found

    Improved dynamical particle swarm optimization method for structural dynamics

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    A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

    Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach

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    In this paper an image outlier technique, which is a hybrid model called SVM regression based DWT optimization have been introduced. Outlier filtering of RGB image is using the DWT model such as Optimal-HAAR wavelet changeover (OHC), which optimized by the Least Square Support Vector Machine (LS-SVM) . The LS-SVM regression predicts hyper coefficients obtained by using QPSO model. The mathematical models are discussed in brief in this paper: (i) OHC which results in better performance and reduces the complexity resulting in (Optimized FHT). (ii) QPSO by replacing the least good particle with the new best obtained particle resulting in 201C;Optimized Least Significant Particle based QPSO201D; (OLSP-QPSO). On comparing the proposed cross model of optimizing DWT by LS-SVM to perform oulier filtering with linear and nonlinear noise removal standards

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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    Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

    Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

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    A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine

    Prototype centric (PC) software development process model: A machine learning based Hybrid Software Development Model

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    Here in this paper we propose a Machine learning technique based Hybrid software development process model called prototype centric, in short can refer as PC. The proposed hybrid model works by considering any one or more traditional models as source models. We also conduct empirical study to analyze the performance of the PC over other traditional models that are most frequently quoted in literature
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