This paper presents an approach based on NURBS (non-uniform rational B-splines) to achieve a seismic response surface (SRS) from a group of points obtained by using an analytical model of RC joints. NURBS based on the genetic algorithm is an important mathematical tool and consists of generalizations of Bezier curves and surfaces and B-splines. Generally, the accuracy of the design process of joints depends on the number of control points that are captured in the results of experimental research on real specimens. The values obtained from the specimens are the best tools to use in seismic analysis, though more expensive when compared to values simulated by SRSs. The SRS proposed in this paper can be applied to obtain surfaces that show site effect results on destructions of beam-column joint, taking into account different site conditions for a specific earthquake. The efficiency of this approach is demonstrated by the retrieval of simulated-versus-analytical results.

Scheduling of multiobjective problems has gained the interest of the researchers. Past many

decades, various classical techniques have been developed to address the multiobjective problems,

but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search

method and many more are being successfully used. Researchers have reported that hybrid

of these algorithms has increased the efficiency and effectiveness of the solution. Genetic

algorithms in conjunction with Pareto optimization are used to find the best solution for

bi-criteria objectives. Numbers of applications involve many objective functions, and application

of the Pareto front method may have a large number of potential solutions. Selecting

a feasible solution from such a large set is difficult to arrive the right solution for the decision

maker. In this paper Pareto front ranking method is proposed to select the best parents for

producing offspring’s necessary to generate the new populations sets in genetic algorithms.

The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling

process is solved using genetic algorithm based Pareto front ranking method. The algorithm

is coded in Matlab, and simulations were carried out for the crossover probability of 0.6,

0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent

for a crossover probability of 0.6.

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.

In this study, emulsified kerosene was investigated to improve the flotation performance of ultrafine coal. For this purpose, NP-10 surfactant was used to form the emulsified kerosene. Results showed that the emulsified kerosene increased the recovery of ultrafine coal compared to kerosene. This study also revealed the effect of independent variables (emulsified collector dosage (ECD), frother dosage (FD) and impeller speed (IS)) on the responses (concentrate yield (γC %), concentrate ash content ( %) and combustible matter recovery (ε %)) based on Random Forest (RF) model and Genetic Algorithm (GA). The proposed models for γC %, % and ε% showed satisfactory results with R2. The optimal values of three test variables were computed as ECD = 330.39 g/t, FD = 75.50 g/t and IS = 1644 rpm by using GA. Responses at these experimental optimal conditions were γC % = 58.51%,  % = 21.7% and ε % = 82.83%. The results indicated that GA was a beneficial method to obtain the best values of the operating parameters. According to results obtained from optimal flotation conditions, kerosene consumption was reduced at the rate of about 20% with using the emulsified kerosene.

Recently, there has been research on high frequency dissipative mufflers. However, research on shape optimization of hybrid mufflers that reduce broadband noise within a constrained space is sparse. In this paper, a hybrid muffler composed of a dissipative muffler and a reactive muffler within a constrained space is assessed. Using the eigenvalues and eigenfunctions, a coupling wave equation for the perforated dissipative chamber is simplified into a four-pole matrix form. To efficiently find the optimal shape within a constrained space, a four-pole matrix system used to evaluate the acoustical performance of the sound transmission loss (STL) is evaluated using a genetic algorithm (GA).

A numerical case for eliminating a broadband venting noise is also introduced. To verify the reliability of a GA optimization, optimal noise abatements for two pure tones (500 Hz and 800 Hz) are exemplified. Before the GA operation can be carried out, the accuracy of the mathematical models has been checked using experimental data. Results indicate that the maximal STL is precisely located at the desired target tone. The optimal result of case studies for eliminating broadband noise also reveals that the overall sound power level (SWL) of the hybrid muffler can be reduced from 138.9 dB(A) to 84.5 dB(A), which is superior to other mufflers (a one-chamber dissipative and a one-chamber reactive muffler). Consequently, a successful approach used for the optimal design of the hybrid mufflers within a constrained space has been demonstrated.

The growth in the system load accompanied by an increase of power loss in the distribution system. Distributed generation (DG) is an important identity in the electric power sector that substantially overcomes power loss and voltage drop problems when it is coordinated with a location and size properly. In this study, the DG integration into the network is optimally distributed by considering the load conditions in different load models used to surmount the impact of load growth. There are five load models tested namely constant, residential, industrial, commercial and mixed loads. The growth of the electrical load is modeled for the base year up to the fifth year as a short-term plan. Minimization of system power loss is taken as the main objective function considering voltage limits. Determination of the location and size of DG is optimally done by using the breeder genetic algorithm (BGA). The proposed studies were applied to the IEEE 30 radial distribution system with single and multiple placement DG scenarios. The results indicated that installing an optimal location and size DG could have a strong potential to reduce power loss and to secure future energy demand of load models. Also, commercial load requires the largest DG active injection power to maintain the voltage value within tolerable limits up to five years.

In the last decade a growing interest was observed in low-cost adsorbents for heavy metal ions. Clinoptilolite is a mineral sorbent extracted in Poland that is used to remove heavy metal ions from diluted solutions. The experiments in this study were carried out in a laboratory column for multicomponent water solutions of heavy metal ions, i.e. Cu(II), Zn(II) and Ni(II). A mathematical model to calculate the metals' concentration of water solution at the column outlet and the concentration of adsorbed substances in the adsorbent was proposed. It enables determination of breakthrough curves for different process conditions and column dimensions. The model of process dynamics in the column took into account the specificity of sorption described by the Elovich equation (for chemical sorption and ion exchange). Identification of the column dynamics consisted in finding model coefficients β, KE and Deff and comparing the calculated values with experimental data. Searching for coefficients which identify the column operation can involve the use of optimisation methods to find the area of feasible solutions in order to obtain a global extremum. For that purpose our own procedure of genetic algorithm is applied in the study.

In most cases, road machines emit both acoustic and vibration energy into the fluids or structures surrounding the machinery. This is dangerous for the construction strength of the machinery, and is harmful for human health. There are two general classes of tools used to assess and optimize acoustic performance of a vehicle: test based methods, and Computer-Aided Engineering based methods. The second one is discussed in this paper.

The paper presents a model of an articulated vehicle with a flexible frame of a semi-trailer. The rigid finite element method in a modified formulation is used for discretisation of the frame. In order to carry out effective numerical simulation, a reduced model with a considerably smaller number of degrees of freedom is proposed. The parameters of the reduced model are chosen in an optimization process by using a genetic algorithm. To this end, it is assumed that the full and reduced model have to be similar in the range of static deflections and frequencies of free vibrations. Numerical simulations are concerned with the influence of the flexibility of the frame on the motion of the articulated vehicle during an overtaking maneuver. Results are presented and discussed.

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.