Asynchronous Swarm Structural Optimization of Satellite Adapter Ring

A numerical framework for modeling anisotropic dielectric elastomers

With the development of tire mechanics and computer technology, tire deformation, rolling resistance, and temperature distribution under rolling conditions may be predicted accurately through finite element analysis (FEA). Deep knowledge of tire fracture and failure behavior may also be obtained by FEA. During the past years, an in‐house finite element program has been developed in our research laboratory which can analyze the tire deformation, stress, and strain under the static inflation and footprint load conditions and can predict the tire rolling resistance and temperature distribution as well. This paper gives a brief description of the mathematical and mechanical foundations of the developed FEA code and the computing procedures, emphasizing the tire material loss model and the calculation procedure of strain energy release rate in tire fracture analysis. Two characteristics of the presented model compared with the published literature are the three‐dimensional anisotropic properties included in the loss model of cord‐rubber materials and a new VCCT (Virtual Crack Closure Technique), which is simple and physically direct, saves on the amount of computation, and is developed to compute the fields of strain energy release rates (Serrs) in the crack front to analyze tire fracture behavior.

In seismic areas, the application of structural dampers becomes compulsory in the design of buildings. There are various types of dampers, such as viscous elastic dampers, viscous fluid dampers, friction dampers, tune mass dampers, yielding/ metallic dampers, and magnetic dampers. All damper systems are designed to protect structural integrities, control damages, prevent injuries by absorbing earthquake energy, and reduce deformation. This paper is a part of research investigating the behaviour of the U-shaped steel damper (as one type of metallic damper) that can be applied to the buildings in seismic areas. The dampers are used as connections between the roof and supporting structure, with the two general purposes. The first is to control the displacement of roof under an earthquake, and the second is to absorb seismic energy through the plasticity of some parts in dampers. If a strong earthquake occurs, the plasticity will absorb the seismic energy; therefore, heavy damage could be avoided from the roof’s mainframes. In this paper, several models of U-shaped steel dampers are introduced. Several parameters, such as elastic stiffness, maximum strength, and energy dissipation, are determined under two conditions. Firstly, static analysis of the proposed damper under variation of U-steel plate configurations, searching the model with more significant energy dissipation. Secondly, static analysis of the unsymmetrical and symmetrical damper under different loading directions. An in-house finite element program that involves both geometrical and material nonlinearities is developed as a problem solver. A quasi-static lateral loading is given to each model until one cycle of the hysteresis curve is reached (in the displacement range between -20 mm to +20 mm). The above parameters are calculated from the hysteresis curve. From the results, the behaviour of the U-steel damper can be described as follows. Firstly, increasing the energy dissipation in the lateral direction can be done by increasing the lateral stiffness of the damper. However, it can reduce the maximum elastic deformation of the damper. Secondly, under the random direction of loading, a symmetrical shape can increase the energy dissipation of the damper.

In the current study, a rate-dependent crystal plasticity finite element method (CPFEM) was used to simulate flow stress behavior and texture evolution of a body-centered cubic (BCC) crystalline material during plastic deformation at room temperature. To account for crystallographic slip and rotation, a rate-dependent crystal constitutive law with a hardening model was incorporated into an in-house finite element program, CAMPform3D. Microstructural heterogeneity and anisotropy were handled by assigning a crystallographic orientation to each integration point of the element and determining the stiffness matrix of the individual crystal. Uniaxial tensile tests of single crystals with different crystallographic orientations were simulated to determine the material parameters in the hardening model. The texture evolution during four different deformation modes - uniaxial tension, uniaxial compression, channel die compression, and simple shear deformation - was investigated based on the comparison with experimental data available in the literature.

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

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.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

In the view of the problem of tuning the parameters of the fuzzy logic controller (FLC) for Delta robot trajectory control, a method is put forward to tune the parameters of membership functions and the gains of the controller with particle swarm optimization (PSO). A PSO with dynamic parameters is proposed which makes the PSO has better performance in global search and faster convergence. The fitness function of the PSO was designed by using the time domain indices and integral of time-weighted-squared-error (ITSE) to optimize the performance of the controller. The simulation results show that FLC tuned by PSO has better performance and more robust than the PSO tuned PID controller for the DELTA robot trajectory control.

With the development of digital power systems, the establishment of digital twin models for transformers is of great significance in enhancing power system stability. Consequently, greater demands are placed on the real-time performance and accuracy of thermal-fluid-coupled transient multi-physics field calculations for transformers. However, traditional numerical methods, such as finite element or computational fluid dynamics techniques, often require days or even weeks to simulate full-scale high-fidelity transformer models containing millions of grid nodes. The high computational cost and long runtime make them impractical for real-time simulations in digital twin applications. To address this, this paper employs the dynamic mode decomposition (DMD) method in conjunction with Koopman operator theory to perform data-driven reduced-order modeling of the transformer’s thermal–fluid-coupled multi-physics field. A fast computational approach based on extended dynamic mode decomposition (EDMD) is proposed to enhance the modal decomposition capability of nonlinear systems and improve prediction accuracy. The results show that this method greatly improves computational efficiency while preserving accuracy in high-fidelity models with millions of grids. The errors in the thermal and flow field calculations remain below 3.06% and 3.01%, respectively. Furthermore, the computation time is reduced from hours to minutes, with the thermal field achieving a 97-fold speed-up and the flow field an 83-fold speed-up, yielding an average speed-up factor of 90. This enables fast computation of the transformer’s thermal–fluid-coupled field and provides effective support for the application of digital twin technology in multi-physics field simulations of power equipment.

This paper presents an efficient approach for solving economic dispatch(ED) problem with non-convex cost functions using particle swarm optimization with time varying acceleration coefficients. PSO_TVAC is applied to 3, 13, & 14 unit test system with and without valve point loading effects. The results of PSO_TVAC are compared with PSO From simulation results, it is clear that PSO_TVAC performance is better than many well settled nature inspired algorithms.Economic dispatch is one of the key functions of the modern energy management system. Classical particle swarm optimization (PSO) algorithm is capable of achieving near global solutions for such problems but it tends to converge prematurely. The practical Non Convex Economic Dispatch problem is solved here using PSO with a novel parameter automation strategy in which time varying acceleration coefficients (TVAC) are employed to efficiently control the local and global search, such that premature convergence is avoided and global solutions are achieved.

Optimization techniques that search a solution space without designer intervention are becoming important tools in the engineering design of many thermal fluid systems. Evolutionary algorithms are among the most robust of these optimization methods because the ability to optimize many designs simultaneously makes evolutionary algorithms less susceptible to premature convergence. However application of evolutionary algorithms to thermal and fluid systems described by high fidelity models (e.g. computational fluid dynamics) has been limited due to the high computational cost of the fitness evaluation. This paper presents a novel technique that combines two technologies used in the optimization of thermal fluids systems. The first is graph based evolutionary algorithms that are implemented to help increase the diversity of the evolving population of designs. The second is an algorithm utilizing a feed forward neural network that develops a stopping criterion for computational fluid dynamics solutions. This reduces the time required for each future evaluation in the evolutionary process and allows for more complex thermal fluids systems to be optimized. In the system examined here the overall reduction in computational time is approximately 8 times.

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

Particle Swarm Optimization (PSO) deteriorates when facing a high-noise environment. To address this issue, one popular mechanism is the resampling method that is based on re-evaluations to find the true fitness value. However, the budget for re-evaluations in PSO is limited. In this paper, we intend to integrate a Stochastic Point Location (SPL) method into PSO to alleviate the impacts of noise on the evaluation of true fitness. SPL deals with the problem of a learning mechanism locating a target point on the line in noisy environment. Up to now, Adaptive Step Searching is the fastest algorithm in solving the SPL problem and shows great anti-noise performance. This paper investigates two effective hybrid PSO approaches, by integrating PSO and PSO-Equal Resampling with Adaptive Step Searching. The simulation results and comparisons on 20 large-scale benchmark optimization functions in noisy environments demonstrate the superiority of the proposed approaches in terms of optimization accuracy and convergence rate.