A new approach to optimize variable axial composite shells: A metaheuristic procedure using the finite element method

Free Vibrations of Composite Shallow Circular Cylindrical Shell Panels Stiffened by A Bonded Non-Central Shell Strip

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Efficient global optimization of aerodynamic shapes with the high-fidelity method is of great importance in the design process of modern aircrafts. In this study, modifications are made to the particle swarm optimization (PSO) algorithm and the radial basis function (RBF) model for further improvements of efficiency and accuracy of optimization. Specifically, a PSO algorithm with randomly distributed cognitive and social parameters, exponential decrease of maximum velocity and inertia weight, and periodic mutation of particle position is proposed. Furthermore, a nonuniform shape parameters strategy is introduced for the RBF surrogate model. Validations on test functions show that the new PSO has remarkable speed of convergence, and the new RBF model has superior approximation accuracy. The PSO algorithm and RBF model are then combined to construct the computational fluid dynamics (CFD)–based optimization framework. Finally, optimizations of transonic airfoil and supersonic launch vehicle are perfo...

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

The present paper proposes a fault diagnosis methodology of three phase inverter circuit base on radial basis function (RBF) artificial neural network trained by particle swarm optimization (PSO) algorithm. Using the appropriate stimulus signal, fault features are extracted from efficient points in frequency response of the circuit directly and then a fault dictionary is created by collecting signatures of different fault conditions. Trained by the examples contained in the fault dictionary, the RBF neural network optimized by PSO has been demonstrated to provide robust diagnosis to the difficult problem of soft faults in three phase inverter circuits. The experimental result shows that the proposed technique is succeeded in diagnosing and locating faults effectively. Keyword: three phase inverter circuit, RBF neural network, fault diagnosis, particle swarm optimization.

Time-frequency methods for vibration-based gearbox faults detection have been considered the most efficient method. Among these methods, continuous wavelet transform (CWT) as one of the best time-frequency method has been used for both stationary and transitory signals. Some deficiencies of CWT are problem of overlapping and distortion ofsignals. In this condition, a large amount of redundant information exists so that it may cause false alarm or misinterpretation of the operator. In this paper a modified method called Exact Wavelet Analysis is used to minimize the effects of overlapping and distortion in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm Optimization (PSO) algorithm has been used for this purpose. This method have been implemented for the acceleration signals from 2D acceleration sensor acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir University of Technology. Gearbox has been considered in both healthy and chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with radial basis functions has used the extracted features from exact wavelet analysis for classification. The efficiency of this classifier is then evaluated with the other signals acquired from the setup test. The results show that in comparison of CWT, PSO Exact Wavelet Transform has better ability in feature extraction in price of more computational effort. In addition, PSO exact wavelet has better speed comparing to Genetic Algorithm (GA) exact wavelet in condition of equal population because of factoring mutation and crossover in PSO algorithm. SVM classifier with the extracted features in gearbox shows very good results and its ability has been proved.

In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.

Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 ) ), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

In current study, natural frequency response of fiber metal laminated (FML) fibrous composite panels is optimized under different combination of the three classical boundary conditions using particle swarm optimization (PSO) algorithm and finite strip method (FSM). The ply angles, numbers of layers, panel length/width ratios, edge conditions and thickness of metal sheets are chosen as design variables. The formulation of the panel is based on the classical laminated plate theory (CLPT), and numerical results are obtained by the semi-analytical finite strip method. The superiority of the PSO algorithm is demonstrated by comparing with the simple genetic algorithm.

Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to particle swarm optimization (PSO) algorithm, which increases the search capability of PSO algorithm considerably. The proposed algorithm initializes a new search with harmony pitch adjusting or random selection when PSO search direction is estimated incorrectly. This can provide further opportunities of finding better solutions for the particle swarm by guiding the entire particle swarm to promising new regions of the search space and accelerating the search. PSO, HPSO and IHPSO, as well as other advanced PSO procedures from the literature were compared on several benchmark test functions extensively. Statistical analyses of the experimental results indicate that the performance of IHPSO is better than the performance of PSO and HPSO.

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery