Electromagnetic arrangements which create a magnetic field of required distribution and magnitude are widely used in electrical engineering. Development of new accurate designing methods is still a valid topic of technical investigations. From the theoretical point of view the problem belongs to magnetic fields synthesis theory. This paper discusses a problem of designing a shape of a solenoid which produces a uniform magnetic field on its axis. The method of finding an optimal shape is based on a genetic algorithm (GA) coupled with Bézier curves.

The frictional resistance coefficient of ventilation of a roadway in a coal mine is a very important technical parameter in the design and renovation of mine ventilation. Calculations based on empirical formulae and field tests to calculate the resistance coefficient have limitations. An inversion method to calculate the mine ventilation resistance coefficient by using a few representative data of air flows and node pressures is proposed in this study. The mathematical model of the inversion method is developed based on the principle of least squares. The measured pressure and the calculated pressure deviation along with the measured flow and the calculated flow deviation are considered while defining the objective function, which also includes the node pressure, the air flow, and the ventilation resistance coefficient range constraints. The ventilation resistance coefficient inversion problem was converted to a nonlinear optimisation problem through the development of the model. A genetic algorithm (GA) was adopted to solve the ventilation resistance coefficient inversion problem. The GA was improved to enhance the global and the local search abilities of the algorithm for the ventilation resistance coefficient inversion problem.

This paper presents results of evolutionary minimisation of peak-to-peak value of a multi-tone signal. The signal is the sum of multiple tones (channels) with constant amplitudes and frequencies combined with variable phases. An exemplary application is emergency broadcasting using widely used analogue broadcasting techniques: citizens band (CB) or VHF FM commercial broadcasting. The work presented illustrates a relatively simple problem, which, however, is characterised by large combinatorial complexity, so direct (exhaustive) search becomes completely impractical. The process of minimisation is based on genetic algorithm (GA), which proves its usability for given problem. The final result is a significant reduction of peak-to-peak level of given multi-tone signal, demonstrated by three real-life examples.

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

An important operational task for thermal turbines during run-up and run-down is to keep the stresses in the structural elements at a right level. This applies not only to their instantaneous values, but also to the impact of them on the engine lifetime. The turbine shaft is a particularly important element. The distribution of stresses depends on geometric characteristics of the shaft and its specific locations. This means a groove manufactured for fixing the rotor blades. The extreme stresses in this place occur during the start-up and the shaft heating to normal operating temperature. The process needs optimisation. Optimization tasks are multidisciplinary issues and can be carried out using different methods. In recent years, particular attention in optimisation has been paid to the use of artificial intelligence methods. Among them, a special role is assigned to genetic algorithms. The paper presents a genetic algorithm method to optimise the steam turbine shaft heating process during its start-up phase. The presented optimization task of this algorithm is to carry out the process of the shaft heating as soon as possible at the conditions of not exceeding the stresses at critical locations at any heating phase.

The multi-phase permanent-magnet machines with a fractional-slot concentrated-winding (FSCW) are a suitable choice for certain purposes like aircraft, marine, and electric vehicles, because of the fault tolerance and high power density capability. The paper aims to design, optimize and prototype a five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet motor. To optimize the designed multi-phase motor a multi-objective optimization technique based on the genetic algorithm method is applied. The machine design objectives are to maximize torque density of the motor and maximize efficiency then to determine the best choice of the designed machine parameters. Then, the two-dimensional Finite Element Method (2D-FEM) is employed to verify the performance of the optimized machine. Finally, the optimized machine is prototyped. The paper found that the results of the prototyped machine validate the results of theatrical analyses of the machine and accurate consideration of the parameters improved the acting of the machine.

One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (2D) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the Binary Bat Algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.

The article presents a new optimization tool supporting supply chain management in the multi-criteria aspect. This tool was implemented in the EPLOS system (European Logistics Services Portal system). The EPLOS system is an integrated IT system supporting the process of creating a supply and distribution network in supply chains. This system consists of many modules e.g. optimization module which are responsible for data processing, generating results. The main objective of the research was to develop a system to determine the parameters of the supply chain, which affect its efficiency in the process of managing the goods flow between individual links in the chain. These parameters were taken into account in the mathematical model as decision variables in order to determine them in the optimization process. The assessment of supply chain management effectiveness was carried out on the basis of the global function of the criterion consisting of partial functions of the criteria described in the mathematical model. The starting point for the study was the assumption that the effectiveness of chain management is determined by two important decision-making problems that are important for managers in the supply chain management process, i.e. the problem of assigning vehicles to tasks and the problem of locating logistics facilities in the supply chain. In order to solve the problem, an innovative approach to the genetic algorithm was proposed, which was adapted to the developed mathematical model. The correctness of the genetic algorithm has been confirmed in the process of its verification.

Numerical predictions of heat transfer under laminar conditions in a square duct with ribs are presented in this paper. Ribs are provided on top and bottom walls in a square duct in a staggered manner. The flow rates have been varied between Reynolds number 200 and 600. Various configurations of ribs by varying length, width and depth have been investigated for their effect on heat transfer, friction factor and entropy augmentation generation number. Further artificial neural network integrated with genetic algorithm was used to minimize the entropy augmentation generation number (performance factor) by selecting the optimum rib dimensions in a selected range. Genetic algorithm is compared with microgenetic algorithm to examine the reduction in computational time for outlay of solution accuracy.