Search results

The purpose of this paper is to present a new calculation method for increasing the shielded volume in which the external electromagnetic field is maximally reduced. In a space shielded in the way mentioned in this paper, it is possible to introduce measurement instruments and increase the accuracy of results obtained with them, as well as reduce the risk of unwanted electrostatic field influence on living organisms.

A new numerical procedure for the optimization of the coaxial ring conductor system for electrostatic shielding is developed in the paper. The optimization of the functional that consists of electrostatic energy density and a system of equations derived from the equipotential character of the conductor system is used. The system of nonlinear equations is obtained and then numerically solved by minimizing this functional. The first presented optimization procedure is based on the analytical optimization method using the Lagrange coefficients and gradient of the objective function.

It is possible to design a large number of protective ring formations. Applying the differential evolution optimization method, an optimal arrangement can be obtained for any specific number of rings. The differential evolution optimization method, which belongs to the class of evolutionary algorithms, is used for solving this very complex optimization problem. In combination with the above-mentioned method, excellent results in the elimination of the external electric field have been obtained. Although a larger number of rings provides more efficient protection, this number is limited from the economic point of view. Therefore, it is necessary to achieve a compromise between the number of rings, the size of volume shielded and the quality of protection.

There are few papers that address this problem, although the elimination of the influence of the external electromagnetic field has gained more importance lately. The presented method can be applied to increase the reliability of measured data, protection of the environment, in space research, etc. The main limiting factor for using a larger number of rings that provide better protection is the economical one.

The proposed method is suitable for the generalization of procedures, for the protection of the space where the external electric field needs to be reduced or eliminated.

This paper aims to present a new approach to the numerical solution of skin effect integral equations in cylindrical conductors. An approximate, but very simple and accurate method for calculating the current density distribution, skin-effect resistance and inductance, in pulse regime of cylindrical conductor, having a circular or rectangular cross-section, is considered. The differential evolution method is applied for minimization of error functional. Because of its application in the practice, the lightning impulse is observed. Direct and inverse fast Fourier transform is applied.

This method contributes to increasing of correctness and much faster convergence. As the electromagnetic field components depend on the current density derivation, the proposed method gives a very accurate solution not only for current density distribution and resistance but also for field components and for internal inductance coefficients. Distribution of current and electromagnetic field in bus-bars can be successfully determined if the proximity effect is included together with the skin effect in calculations.

The study shows the strong influence of direct lightning strikes on the distribution of electrical current in cables used in lightning protection systems. The current impulse causes an increase in the current density at all points of the cross-section of the conductor, and in particular the skin effect on the external periphery. Based on the data calculated by using the proposed method, it is possible to calculate the minimum dimensions of the conductors to prevent system failures.

There are a number of approximations of lightning strike impulse in the literature. This is a limiting factor that affects the reliability and agreement between measured data with calculated values.

In contrast with other methods, the current density function is approximated by finite functional series, which automatically satisfy wave equation and existing boundary conditions. It is necessary to minimize the functional. This approach leads to a very accurate solution, even in the case when only two terms in current approximation are adopted.

This paper sets out to show the feasibility of the genetic algorithm inverse method for the determination of the parameters of crystallization kinetics laws in isothermal and non‐isothermal conditions, using multiple experiments.

The mathematical model for crystallization kinetics determination and the numerical methods of its resolution are introduced. Crystallization kinetic parameters determined by approximate physical analysis and the inverse genetic algorithm method are presented. Injection molding simulations taking into account crystallization are performed using the finite element method.

It is necessary to perform the optimization on two parameters, transformed volume fraction and number of spherulites to obtain correct results. It is possible to use results from different samples, in spite of the dispersion of some values.

Experimental data for isothermal and non‐isothermal conditions were used and obtained good results for the parameters of crystallization kinetics laws from which the evolutions of overall crystallization kinetics and crystalline microstructure were deduced. Nevertheless, the dispersion of the experimental data concerning the number of spherulites obtained with different samples is important. The evolution of the number of spherulites is required for the optimization to get correct results.

An important result of this work is that the genetic algorithm optimization can be applied to this problem where the experiments cannot be performed with a single sample and the experimental data for the number of spherulites have low precision. Even if only the crystallization kinetics was considered, the feasibility in molding simulation has been shown.

Simulation of crystallization in injection molding is very important for a later prediction of the end‐use properties.

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting with each other. This paper aims to treat the transformer design optimization (TDO) as a multiobjective problem (MOP), to minimize the manufacturing cost and the total owing cost, taking into consideration design constraints.

To deal with this optimization problem, a new method is proposed that combines the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) with differential evolution, also applying lognormal distribution for tuning the scale factor and the beta distribution to adjust the crossover rate (UPS-DELFBC). The proposed UPS-DELFBC is useful to maintain the adequate diversity in the population and avoid the premature convergence during the generational cycle. Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

This paper develops a promising UPS-DELFBC approach to solve MOPs. The TDO problems for three different transformer specifications, with 160, 400 and 630 kVA, have been addressed in this paper. Optimization results show the potential and efficiency of the UPS-DELFBC to solve multiobjective TDO and to produce multiple Pareto solutions.

The purpose of this paper is to reduce the computational burden and improve the precision of the parameter optimization in the convection-diffusion equation, a new algorithm, the refined gray-encoded evolution algorithm (RGEA), is proposed.

In the new algorithm, the differential evolution algorithm (DEA) is introduced to refine the solutions and to improve the search efficiency in the evolution process; the rapid cycle operation is also introduced to accelerate the convergence rate. The authors apply this algorithm to parameter optimization in convection-diffusion equations.

Two cases for parameter optimization in convection-diffusion equations are studied by using the new algorithm. The results indicate that the sum of absolute errors by the RGEA decreases from 74.14 to 99.29 percent and from 99.32 to 99.98 percent, respectively, compared to those by the gray-encoded genetic algorithm (GGA) and the DEA. And the RGEA has a faster convergent speed than does the GGA or DEA.

A more complete convergence analysis of the method is under investigation. The authors will also explore the possibility of adapting the method to identify the initial condition and boundary condition in high-dimension convection-diffusion equations.

This paper will have an important impact on the applications of the parameter optimization in the field of environmental flow analysis.

This paper will have an important significance for a sustainable social development.

The authors establish a new RGEA algorithm for parameter optimization in solving convection-diffusion equations. The application results make a valuable contribution to the parameter optimization in the field of environmental flow analysis.

Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.

The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.

A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.

A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

Monitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task.

Population-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function.

The results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage.

The value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance.

Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.

In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.

This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.

Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.

In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.

In this work, the multi-objective optimization shuffled complex evolution is proposed. The algorithm is based on the extension of shuffled complex evolution, by incorporating two classical operators into the original algorithm: the rank ordering and crowding distance. In order to accelerate the convergence process, a Local Search strategy based on the generation of potential candidates by using Latin Hypercube method is also proposed.

The multi-objective optimization shuffled complex evolution is used to accelerate the convergence process and to reduce the number of objective function evaluations.

In general, the proposed methodology was able to solve a classical mechanical engineering problem with different characteristics. From a statistical point of view, we demonstrated that differences may exist between the proposed methodology and other evolutionary strategies concerning two different metrics (convergence and diversity), for a class of benchmark functions (ZDT functions).

The development of a new numerical method to solve multi-objective optimization problems is the major contribution.

To present and validate a new differential evolution (DE) method for multi‐objective optimization method.

A new selection scheme was designed to replace the existing one in DE to enable DE applicable to either single objective or multi‐objective optimizations.

The new method was validated with three simple multi‐objective optimization problems. The simulation results show that the approach is capable of generating an approximated Pareto‐front for each selected problem. The new DE method was used to optimize a prototype air mixer subject to two objective functions to be minimized. The results demonstrate that the new DE approach can handle this practical multi‐objective problem successfully.

The new method is an easy‐to‐implement evolutionary method and has the potential for application for any complicated engineering optimizations.