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Typical multilevel inverter staircase waveform output represented as a function of angles and switching times.
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Selective harmonics elimination (SHE) is a widely applied control strategy in multilvel inverters for harmonics reduction. SHE is designed for the elimination of low-order harmonics while keeping the fundamental component equal to any previously specified amplitude. This paper proposes a novel bio-inspired metaheuristic optimization algorithm calle...
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The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphi...
![Fig. 4. SSO flowchart [25].](profile/Saman-Almufti/publication/352792354/figure/fig1/AS:1039677641457666@1624889874375/SSO-flowchart-25_Q320.jpg)
![Fig. 5. SSORS flowchart [25].](profile/Saman-Almufti/publication/352792354/figure/fig2/AS:1039677641465857@1624889874401/SSORS-flowchart-25_Q320.jpg)
The continues in real-world problems increasing complexity motivated computer scientists and researchers to search for more-efficient problem-solving strategies. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optim...
Power converters are electronic devices widely applied in industry, and in recent years, for renewable energy electronic systems, they can regulate voltage levels and actuate as interfaces, however, to do so, is needed a controller. Proportional-Integral-Derivative (PID) are applied to power converters comparing output voltage versus a reference vo...
This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to...
The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments....
Citations
... The missing spiral search strategy makes it perform mediocrely in the global search process, although it performs outstandingly in continuous unimodal functions. This study introduced the spiral motion behavior of spiders in the black widow algorithm [42] into the global search to strengthen the ability of the Harris Eagle algorithm to jump out of local optima, and the improvement also be used to characterize the circling behavior of Harris Hawks during the global search. ...
This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm.
... Mirjalili et al. [29] used the Grey Wolf Optimized method (GWO), initially developed to study wolves hunting strategies. Another method developed by Peña-Delgado et al. [30], inspired by the spider's mating strategies and named the Black Widow Optimization Algorithm (BWOA) was used to solve an electronic problem. A similar algorithm inspired by spiders hunting strategies was proposed by Peraza-Vázquez et al. [28] as a Jumping Spider Optimization Algorithm (JSOA). ...
With the growing global energy consumption, on the first hand, and the need to produce low temperatures on the second hand, therefore the technological challenges consist in minimizing greenhouse gas emissions by reducing the energy consumption. In fact, in refrigeration, this consists of the use of environmentally friendly refrigerants (GWP < 150) with the optimization of cold production processes. In this context, several optimization methods have been tested, and the objective of this work to seek the most reliable method to optimize a three-stage cascade vapor compression refrigeration system. Regarding refrigerants, hydrocarbons can be a solution, the R50/R1150/R290 and R50/R1150/R1270 groups are selected and used here to produce temperatures ranging from −160 °C to −120 °C. After comparing seven (7) nature-inspired optimization methods, the JSOA bio-inspired optimization method is applied. Energetic, exergetic and environmental analyzes are also applied to determine the optimal operating point that allows both the minimization of energy consumption and TEWI (Total Equivalent Warming Impact). A pragmatic comparison between the two considered groups provides insights into the choice of high-temperature sub-cycle fluid, and it is found that R290 shows better results compared to R1270.
... First, the refrigerant flow rates of the LT and HT loops are given, respectively, by Eqs. (35) and (36). Then, the powers of the LT and HT compressors as well as the power of the condenser are calculated by Eqs. ...
... Mirjalili et al. [35] used the Grey Wolf Optimised (GWO), a method initially developed to study wolves hunting strategies. Another method developed by Peña-Delgado et al. [36], inspired by the spiders' mating strategies and dubbed the Black Widow Optimisation Algorithm (BWOA), was used to resolve an electronic problem. A similar algorithm inspired by spiders hunting strategies was proposed by Peraza-Vázquez et al. [34] as jumping spider optimisation algorithm (JSOA). ...
Refrigeration at low temperatures is an important requirement for long storage duration where compression cascade systems can offer very efficient and eco-friendly solution opportunities in the context of the current environmental preoccupations, by using innocuous refrigerants pairs as working fluids. In the present work, energy- and exergy-based modelling is performed for fluid pairs of natural refrigerants with low or no GWP and ODP effects. R744/R717, R744/R290, R744/R600, R744/R600a, R744/R1270 pairs are considered, where the low-temperature loop (LT) is filled with carbon dioxide, while the high-temperature loop (HT) employs either ammonia, propane, butane, isobutene or propylene. Optimised system performance is assessed for the effect of temperature variations in the evaporator–condenser. To this aim, several optimisation methods are evaluated to best determine the evaporation temperature in the HT loop that maximises the system’s COP. With the suitable optimising method selected, optimal operating conditions are successively computed for each refrigerant pair, and the resulting performances are determined for condensation and evaporation temperatures in the respective ranges of 25–50 °C and − 55 to − 30 °C. It can be observed that for these conditions, the highest optimised performances are achieved with the R744/R1270 and R744/R717 pairs.
... Inspired by the hunting behavior of black widow spiders, characterized by both linear and spiral movements within their webs, the BWOA offers advantages in both local exploitation and global exploration (Hayyolalam and Pourhaji Kazem, 2019;Peña-Delgado et al., 2020). Population initialization, reproduction, intraspecific predation, mutation, and population update are its five stages. ...
Rehabilitation robots have gained considerable focus in recent years, aiming to assist immobilized patients in regaining motor capabilities in their limbs. However, most current rehabilitation robots are designed specifically for either upper or lower limbs. This limits their ability to facilitate coordinated movement between upper and lower limbs and poses challenges in accurately identifying patients’ intentions for multi-limbs coordinated movement. This research presents a multi-postures upper and lower limb cooperative rehabilitation robot (U-LLCRR) to address this gap. Additionally, the study proposes a method that can be adjusted to accommodate multi-channel surface electromyographic (sEMG) signals. This method aims to accurately identify upper and lower limb coordinated movement intentions during rehabilitation training. By using genetic algorithms and dissimilarity evaluation, various features are optimized. The Sine-BWOA-LSSVM (SBL) classification model is developed using the improved Black Widow Optimization Algorithm (BWOA) to enhance the performance of the Least Squares Support Vector Machine (LSSVM) classifier. Discrete movement recognition studies are conducted to validate the exceptional precision of the SBL classification model in limb movement recognition, achieving an average accuracy of 92.87%. Ultimately, the U-LLCRR undergoes online testing to evaluate continuous motion, specifically the movements of “Marching in place with arm swinging”. The results show that the SBL classification model maintains high accuracy in recognizing continuous motion intentions, with an average identification rate of 89.25%. This indicates its potential usefulness in future rehabilitation robot-active training methods, which will be a promising tool for a wide range of applications in the fields of healthcare, sports, and beyond.
... Considering the challenges posed by the non-linear equations associated with the SHEPWM formulation, a variety of iterative, stochastics, and metaheuristics methods have been utilized [24]- [29]. In the recent algorithms including the Modified Dingo Optimization Algorithm (mDOA) [28], Black Window Optimization Algorithm (BWOA) [32], Grey Wolf Optimization Algorithm (GWOA) [28], Jumping Spider Optimization Algorithm (JSOA) [19], Modified Grey Wolf Optimization Algorithm (MGWOA) [33], Mexican Axolotl Optimization (MAO) [34], Chaos Game Optimization (CGO) [35], Coot Bird Algorithm (COOT) [36], Golden Eagle Optimizer (GEO) [37], and Harris Hawks Optimization (HHO) [38] have gained significant favour due to their proficiency in finding local optima and circumventing stagnation points [30]- [35]. Despite the success of individual metaheuristics methods like the Teaching-Learning Based Optimization (TLBO), Whale Optimization Algorithm (WOA), and others, none of them are free of limitations and constraints [45]. ...
... Optimization Algorithm (mDOA) [28], Black Window Optimization Algorithm (BWOA) [32], Grey Wolf Optimization Algorithm (GWOA) [28], Jumping Spider Optimization Algorithm (JSOA) [19], Modified Grey Wolf Optimization Algorithm (MGWOA) [33], Mexican Axolotl Optimization (MAO) [34], Chaos Game Optimization (CGO) [35], Coot Bird Algorithm (COOT) [36], Golden Eagle Optimizer (GEO) [37], and Harris Hawks Optimization (HHO) [38] have been selected for a detailed comparative analysis in Table 8-10. The nearly optimal angles derived from these algorithms have been input into a Simulink to generate the staircase output waveform. ...
This study presents an innovative hybrid optimization approach that combines teaching-learning based optimization (TLBO) with the whale optimization algorithm (WOA) for selective harmonic elimination (SHE) technique in a modified reduced switch topology three phase multilevel inverter (MLI). The proposed topology requires fewer switches than a conventional cascaded H-bridge MLI and another reduced switch topology in a single phase MLI. Once applied to an 11-level inverter, this hybrid strategy effectively tackles the issues of harmonic reduction and total harmonic distortion (THD) on the line-to-line voltage, significantly improving the quality of the output power through the optimal determination of switching angles. The study leverages the TLBO and WOA to solve the non-linear set of equations associated with the SHE controls technique, aiming to overcome the limitations of classical methods prone to local optimal solutions and dependent on initial controlling parameters. This method has been performed in two steps, during the first step TLBO has been executed and in the next step the solutions derived from TLBO has been used as an initial guess for WOA which ensures the attainment of the precisely converged solution. By using MATLAB®/Simulink software environment, the performance of the hybrid TLBO with WOA method has been simulated and benchmarked against traditional standalone metaheuristic techniques. The simulation results reveal that proposed hybrid approach becomes advantageous in terms of SHE and output voltage quality across various modulation indices. The experimental results verified that the proposed algorithm has been validated through the implementation of a three-phase 11-level inverter. This study highlights the significant potential of the hybrid optimization method in progressing harmonic minimization techniques within the multilevel inverters.
... The majority of intelligent algorithms, like the SMA, are to blame for this. A population made up of dozens of people is defined at the beginning of each algorithm, such as the Mayfly Algorithm [7], the Butterfly Optimization Algorithm [8], the Monarch Butterfly Optimization Algorithm [9], the sparrow search algorithm [6], the Black Widow Optimization Algorithm [10], and so on. Each person in the population represents a random search location point. ...
Our research focused on an optimization algorithm. Our work makes three contributions. First, a new optimization algorithm, the Maritime Search and Rescue Algorithm (MSRA), is creatively proposed. The algorithm not only has better optimization performance, but also has the ability to plan the path to the best site. For other existing intelligent optimization algorithms, it has never been found that they have both of these performances. Second, the mathematical model of the MSRA was established, and the computer program pseudo-code was created. Third, the MSRA was verified by experiments.
... As a component of the strategies related to movement, the spider's motions within the web are simulated using linear and spiral patterns, as outlined in Equation (3) and depicted in Figure 5 [39][40][41]. ...
... Black widow spider movement[39]. ...
The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.
... Fruit y optimization algorithm [131] FOAA 112 Dung beetle optimizer [132] BDO 113 Dragon y algorithm [133] DA 114 Snake optimizer [134] SO 115 Black widow optimization algorithm [135] BWOA 116 Atom search optimization [ Social-spider optimization algorithm [139] SSO 120 Thermal exchange optimization [140] TEO 121 Beetle antennae search algorithm [141] BAS 122 ...
Swarm intelligence algorithms are an important study field of artificial intelligence, and received a lot of attention in the areas, such as parameter optimization, data mining, image processing and signal processing. They draw on the characteristic of social animals that can gather and share the information to make a better decision than individuals. And thus, it makes all the algorithms need an iterative process. For nearly several decades, more than 100 promising algorithms have been proposed. Are these algorithms suitable for all types of problems? How do they relate to the maximum iterations? In this study, 45 test functions from the classical set, CEC2019 and CEC2022 are classified into different problems according to their features, and 123 swarm intelligence algorithms are evaluated on a large scale with different maximum iterations. The experiment results show that most of the algorithms are suitable for low and medium dimensional problems where 5 algorithms (BES, CHOA, ESDA, FBI, and SFS) have the best optimization performance and robustness on these problems. Several algorithms are suitable for the problems with different complexities where 5 algorithms (BES, FA, MPA, SA, and SFS) have the best performance of the problems. Very few algorithms are suitable for the problems with different search space sizes where the CHIO is very robust in the problems. Besides, 3 algorithms (LSO, DE, and RSA) are the fastest.
... Therefore, it is crucial to improve both the global searching capability and the local search capability of the algorithm. In 2020, inspired by the unique mating behavior of the black widow spider, Peña Delgado AF proposed the black widow spider optimization algorithm (BWOA) [27]. In 2015, the moth-flame optimization (MFO) was an algorithm proposed by Seyedali Mirjalili, and was inspired by the laws of nature [28]. ...
... If |S t | ≥ N, all individuals in the F l layer are normalized using Equations (25)- (27), and reference points are generated: ...
Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.
... These offspring remain on their mothers' web for a brief period, during which they may be consumed by their mother. This results in the survival of the strong and fit in which remaining spiders are considered the fittest [44]. The BWO algorithm (Figure 4d) begins with the generation of initial black widow spider population. ...
Agricultural production is becoming progressively susceptible to water scarcity
affecting crop quality and productivity. The implementation of potential evapotranspiration
(PET) methods aids in determining the crop water requirements and improving agricultural water
management. The focus of this study is to identify the optimum meteorological variables
including mean air temperature (Ta), temperature difference(dT), vapor pressure deficit(VPD),
wind speed at two meter-height(U2), net radiation(Rn), and sunshine duration(SD) by employing
bio-inspired metaheuristic techniques (genetic algorithm, moth-flame optimization, grey wolf
optimization, black widow optimization, and sperm swarm optimization). Three PET methods,
Hargreaves, HamonV1, and Penman, were explored to achieve the optimization objective:
minimization of PET parameters and estimates. Penman-Monteith model served as a benchmark
function to evaluate the other PET models. The optimization resulted in parameter values of
20.72ºC Ta, 1.87ºC dT, 11.25h SD, 59.87 MJ/m2/day Rn, 0.1646 kPa VPD, and 0.28 m/s U2.
All swarm intelligence algorithms produced excellent results with near zero(≈0) mean absolute
error(MAE), SSO being the most accurate with 2.5e-6 MAE. The hybrid SSO-Penman method
provided the PET value closest to that of the PM model. This method and optimized values could
serve as motivation for hydrological and agricultural research and applications involving
climatic parameters.
