Image thresholding is a popular method for image segmentation. Histogram is used for image segmentation in image thresholding. In this paper, a MULTILEVEL image thresholding is proposed based on teaching-learning-based OPTIMIZATION (TLBO). TLBO is a new population-based metaheuristic inspired by learners and teacher in a classroom. The optimal thresholds are found by maximizing Kapur’s (entropy criterion) thresholding function. The performance of TLBO is explained by considering five images. In addition, the performance is compared with three well known population-based metaheuristics: particle swarm OPTIMIZATION (PSO), genetic algorithm (GA), and differential evolution (DE).Results show that TLBO presents the better performance in terms of fitness value, peak signal to noise ratio (PSNR), Structural-Similarity index (SSIM), and stability.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex OPTIMIZATION problem that guarantees the global optimal solution. The convex OPTIMIZATION problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

Image segmentation is the process of dividing a digital image into several parts. The segmentation goal is to simplify, or change the representation of an image into something that is both more meaningful and easier to analyze. Thresholding methods with much less complexity are still widely used compared to modern methods based on deep learning. In this paper, a new optimal multi-level thresholding algorithm for histogram-based segmentation of images is presented. The proposed algorithm compared to Particle Swarm OPTIMIZATION Algorithm (PSO) and an improved version of the PSO based on multi-agent fuzzy which is called MAFPSO. In the proposed algorithm, the selection of image thresholding is done using the recently introduced buzzard OPTIMIZATION algorithm (BUZO). In the BUZO algorithm, the process of exploration and exploitation is achieved by defining several types of buzzard with different abilities. MULTILEVEL segmentation is performed-using entropy as a fitness function for BUZO. Comparing the performance of BUZO algorithm with MAFPSO, and PSO for several benchmark images show 8 percent average improvement for fitness function. The quality of segmented images shows 3% in average improvement for 2-level segmented image, and shows 12% in average improvement for 5-level segmented images.

In tube hydroforming, the loading path, that is the relationship between axial feeding and internal fluid pressure, is of important significance. Researchers have employed various OPTIMIZATION approaches to find an optimum loading path. In this research a statistical method based on finite element analysis has been developed. An accurate FEA has been used to simulate the process and to find the response of the process to the loading. The Response Surface Method (RSM) has been used to model the responses from the finite element analysis. The behavior of the process can be predicted using this model. The obtained model then used to optimize the process. Since The RSM model was initially obtained for a predefined domain of variables MULTILEVEL OPTIMIZATION was employed to improve the accuracy of the model. The MULTILEVEL optimized curve yielded the best thickness uniformity, the result of which are reported.

MULTILEVEL image thresholding is essential for segmenting images. Expectation Maximization (EM) is effective for finding thresholds; but, it is sensitive to starting points. The Grey Wolf Optimizer (GWO) is fast at finding thresholds but can get stuck in local optima. This paper presents a new algorithm, EM+GWO, combining both methods to improve segmentation. EM estimates Gaussian Mixture Model (GMM) coefficients, while GWO finds better solutions when EM is stuck. GWO adjusts GMM parameters using Root Mean Square Error (RMSE) for the best fit. The algorithm was tested on nine standard images, evaluating global fitness, PSNR, SSIM, FSIM, and computational time. The results show that EM+GWO significantly enhances image segmentation effectiveness. Statistical tools indicate that RCG achieves the best RMSE and PSNR in 7 out of 9 test images, and it holds the highest rank in both SSIM and FSIM. The average execution time of each algorithm was calculated, showing that EM+GWO has an acceptable running time compared to EM and GWO. This balance between computational efficiency and improved segmentation performance makes the proposed EM+GWO algorithm a robust and effective solution for image segmentation tasks. Overall, the combination of EM and GWO methods provides a more reliable and accurate approach to optimizing image segmentation, avoiding local optima, and enhancing overall performance.

This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge MULTILEVEL inverters (CHB MLI) by employing the Henry Gas Solubility OPTIMIZATION (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the OPTIMIZATION of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the OPTIMIZATION process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex OPTIMIZATION challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.

In this paper, the whale OPTIMIZATION algorithm is proposed for harmonics elimination in a cascaded MULTILEVEL inverter. In selective harmonic elimination pulse width modulation, the selected low-order harmonics are eliminated by solving nonlinear equations, while the fundamental of output waveform is adjusted to a desired value. In this paper, whale OPTIMIZATION algorithm is applied to a 7-level cascaded H-bridge inverter to solve the equations. Also, it was validated by experimental results, since this algorithm has an ability to search in entire solution space, the probability of catching a global best solution is very high. This method has higher accuracy and probability of convergence than the genetic algorithm. The OPTIMIZATION and comparison of whale OPTIMIZATION algorithm and genetic algorithm have been done in MATLAB software. A 1 kW prototype of this converter is built and the results are presented. The effectiveness and the theoretical analysis of this method are verified through both simulation and experimental results.

This paper presents a new method to find the optimum switching angles in voltage source MULTILEVEL converters in order to minimize specific higher order harmonics and decrease the total harmonic distortion (THD) of their output voltage waveform. The output voltage waveform of inverter can either be in the form of staircase or PWM. In order to increase the degrees of freedom and elimination of more harmonics, one can increase the number of levels or use PWM waveform which has more switching than once at each level. However, both of them result in complexity of nonlinear transcendental equations which solving them by using conventional numerical iterative techniques such as Newton-Raphson method is not feasible. In this paper, particle swarm OPTIMIZATION algorithm is presented to find optimum switching angles of PWM waveform in MULTILEVEL converters. These angles must be determined so that the desired fundamental harmonic is maintained and at the same time undesired higher order harmonics are suppressed. Theoretical and simulation results for an eleven-level converter show the efficiency of the proposed algorithm to determine the optimum angles in order to decrease the undesired harmonics and produce very high quality output voltage waveform.

Selective harmonic elimination (SHE) is a powerful modulation scheme aims to find the required switching angles in order to eliminate the number of undesired harmonics. SHE is a complicated problem which consists of several nonlinear equations which have multiple local minima. In order to eliminate the higher number of undesired harmonics and as a result reducing the total harmonic distortion (THD) much more efficiently, the degrees of freedom must be increased. This means that the number of switching angles gets more and as a result, the problem gets more intricate. As the number of switching angles increases, using either traditional iterative techniques or resultant theory method gets useless. So, in this paper, SHE is treated as an OPTIMIZATION problem where Teaching–Learning-Based OPTIMIZATION algorithm (TLBO) is found as an efficient tool to solve it. The provided experimental and simulation results of a 7-level multi-level inverter validate the efficiency and practicability of the implemented scheme.