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.

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.

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.