Search Results (18)

As an innovative metaheuristic algorithm, Genghis Khan Shark Optimization (GKSO) faces challenges, including a tendency towards local optima and poor convergence speed and accuracy. To mitigate these limitations, an improved Genghis Khan shark optimizer (IGKSO) is proposed in this paper. A population partitioning method based on cosine similarity and fitness is introduced, where individuals are strategically assigned to different evolutionary phases: Disadvantaged populations are responsible for the foraging stage. By contrast, advantaged populations dominate the moving stage. In the moving stage, the base vector is randomly selected from multiple candidates, which ensures the evolutionary direction of the population while maintaining its diversity. An adaptive step-size mechanism is introduced to avoid boundary overflow problems. A subspace method is employed to prevent diversity loss during foraging. Additionally, in the hunting stage, a novel opposition-based learning strategy is proposed to moderate the tendency of converging to suboptimal solutions. Furthermore, during the self-protection phase, a criterion for assessing the diversity of the whole population is employed to monitor and supplement diversity in real time. The results of the CEC2017 and CEC2019 benchmark test sets reveal that IGKSO exhibits substantial advantages over the GKSO algorithm and eight other high-performance algorithms in terms of convergence speed and accuracy. Full article

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further enhance the exploitation capability of the standard Starfish Optimization (SFO), the proposed Enhanced Starfish Optimization (ESFO) integrates a fitness-based interacting mechanism within the exploitation phase. This innovative modification improves local search accuracy, preserves population diversity, and mitigates premature convergence without introducing additional control parameters. Moreover, the proposed Enhanced Starfish Optimization (ESFO) is designed for secure wireless transmission, which is considered one of the main topics in next-generation wireless network infrastructure. The investigated network addresses the use of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) in the security of the physical layer. This implemented STAR-RIS has a coupled phase shift to create reflected and transmission links, unlike traditional Reconfigurable Intelligent Surface (RIS). In this regard, we create a safe beamforming architecture that optimizes both Base Station (BS) precoding vectors and STAR-RIS transmission/reflection coefficients. In order to validate the efficiency of the proposed Enhanced Starfish Optimization (ESFO) algorithm, it is compared to several benchmark optimizers such as standard Starfish Optimization (SFO), Dhole Optimizer (DO), Neural Network Algorithm (NNA), Crocodile Ambush Optimization Algorithm (CAOA), and white shark Optimizer (WSO). These comparisons include several scenarios based on the transmitted power threshold which is varied in the range of 20 to 70 dBm with step of 5 dBm. The simulation results show that the proposed Enhanced Star Fish Optimization (ESFO) algorithm consistently outperforms existing benchmark approaches. This study supports future intelligent communication infrastructures in terms of secrecy and achievable rates over a range of transmit power levels. In particular, ESFO improves performance by up to 20–25% while converging 40–50% faster than traditional optimization algorithms, demonstrating its usefulness and resilience in STAR-RIS-assisted secure communication systems. The suggested ESFO-enabled architecture outperforms standard RIS-based systems in terms of secrecy capacity, according to numerical studies, and low-resolution STAR-RIS phase-shifters are sufficient to ensure robust secrecy performance. Full article

Understanding life history parameters is key to assessing demography, biological productivity, and extinction risk of fishes. Age and growth analyses in chondrichthyan fishes (sharks, rays, and ghost sharks) is primarily undertaken through counting vertebral band pairs. For rare, threatened, and protected species such as river sharks (Carcharhinidae; Glyphis), obtaining sufficient vertebrae samples may not be possible. Here we use a very small sample size, selective size-class sampling, back-calculation techniques, and a Bayesian hierarchical model that accounts for repeated measures to provide age and growth information for the Speartooth Shark Glyphis glyphis from which comprehensive sampling is not possible. Ten individuals were selectively sampled from the Adelaide River, Northern Territory, Australia. Bayesian length-at-age models using a combination of informative and uninformative priors in a multi-model framework were applied to the observed and back-calculated data with the sexes combined. Band pair counts produced age estimates of 0–11 years and suggest that age at maturity is possibly >12 years. Most model parameter estimates for length-at-birth (L₀) and asymptotic length (L_∞) were biologically plausible. The Gompertz growth function, applied through a Bayesian hierarchical approach to back-calculated data, provided the best fitting and most biologically appropriate length-at-age parameters: L_∞ = 229.5 cm TL ± (14.6 SE), g_Gom = 0.16 yr⁻¹ ± (0.01 SE), and L₀ = 58.2 cm TL ± (1.4 SE). The results presented here are the first study to apply Bayesian methods to back-calculated length-at-age data while accounting for repeated measures. Full article

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably. Full article

This paper presents rooftop automobile antennas designed for Long-Term Evolution (LTE), Wireless Local Area Network (WLAN) and navigation system applications. The proposed antennas are housed within a shark-fin structure on the car’s roof, and comprise a main antenna and a diversity antenna. The main antenna and diversity antenna combine for spatial diversity, receiving and processing the same signal to optimize signal quality. To accommodate the limited space within the shark-fin housing, various miniaturization and multiband techniques are utilized. The hexagonal substrate is more closely fitted to the shape of the shark fin, thus making full use of the space of the shark-fin shell. The main antenna and the diversity antenna are perpendicular to each other, which saves the space of the overall antenna and improves the utilization rate of the overall antenna space. The proposed main antenna, compactly sized at 50 mm × 20 mm × 1.59 mm, maintains a VSWR value below 2 across the frequency range of 1.19–2.8 GHz, enabling support for LTE bands 1, 2, 3, 4, 7, 11, 15, 16, 34, 39, 40, and 41, as well as WLAN 2400 bands. The diversity antenna maintains a VSWR value below 2 across the frequency range of 1.5–2.6 GHz, which can cover BeiDou B1-1, LTE 1, 2, 3, 4, 7, 11, 15, 16, 34, 39, and 40, and WLAN 2400 bands. The main antenna and the diversity antenna both demonstrate favorable radiation patterns on the azimuth plane. Simulation and measurement results exhibit a high level of agreement. Full article

The frequent fluctuation of pork prices has seriously affected the sustainable development of the pork industry. The accurate prediction of pork prices can not only help pork practitioners make scientific decisions but also help them to avoid market risks, which is the only way to promote the healthy development of the pork industry. Therefore, to improve the prediction accuracy of pork prices, this paper first combines the Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification and Regression Trees (CART), to establish an SSA-CART optimization model for predicting pork prices. Secondly, based on the Sichuan pork price data during the 12th Five-Year Plan period, the linear correlation between piglet, corn, fattening pig feed, and pork price was measured using the Pearson correlation coefficient. Thirdly, the MAE fitness value was calculated by combining the validation set and training set, and the hyperparameter “MinLeafSize” was optimized via the SSA. Finally, a comparative analysis of the prediction performance of the White Shark Optimizer (WSO)-CART model, CART model, and Simulated Annealing (SA)-CART model demonstrated that the SSA-CART model has the best prediction of pork price (compared with a single decision tree, R₂ increased by 9.236%), which is conducive to providing support for pork price prediction. The accurate prediction of pork prices with an optimized machine learning model is of great practical significance for stabilizing pig production, ensuring the sustainable growth of farmers’ income, and promoting sound economic development. Full article

Bioenergetic models are tools that allow the evaluation of the effect of environmental variables on fish growth. Successful implementation of this approach has been achieved in a few elasmobranch species. Our objective was to develop a bioenergetic model for Mustelus schmitti. The model developed showed a good fit to the field data available and accurately described the growth of this species. The practical example developed in this study provides novel population estimates of prey consumption and daily ration for the species. Results also indicate that this species would be susceptible to the effects of climate change. In the simulated climate change scenarios, the energy budget of M. schmitti was significantly altered, with increased food consumption and impaired growth. While there exists a number of limitations for the model developed in this article, namely its limitation to immature individuals, and its restricted temperature model, it provides an important tool for the management of this and other shark populations under heavy exploitation. Full article

This study investigates the substantial contribution of the recent numerical optimization technique known as the White Shark Optimizer (WSO) to evaluate the performance of proton exchange membrane fuel cell (PEMFC) design parameters that play a considerable role in boosting its effectiveness. A numerical code was developed and implemented via MATLAB software to achieve the research goal. The proposed WSO was employed to identify the unknown parameters of the PEMFC equivalent circuit, considering experimental data. The analyzed objective function was the root mean squared error (RMSE) between the measured and estimated fuel cell terminal voltages. Additionally, the proposed WSO was compared with other intelligent approaches such as the salp swarm algorithm (SSA), Harris hawks optimization (HHO), atom search optimization (ASO), dung beetle optimization algorithm (DBOA), stochastic paint optimizer (SPO), and comprehensive learning Archimedes optimization algorithm (HCLAOA). The numerical simulations revealed that the RMSE values varied between lower and higher values of 0.009095329 and 0.028663611, respectively. Additionally, the results indicated that the mean fitness value recorded in the considered PEMFC 250 W stack was 0.020057775. Moreover, the minimum fitness value was obtained using the proposed WSO, with an operating temperature of 353.15 K and working anode and cathode pressures are 3 bar and 5 bar, respectively. The proposed WSO offered the best results in terms of absolute errors compared to the other optimizers, confirming the robustness of the results in all considered cases. Full article

Constructing a reliable equivalent circuit of Li-Ion batteries using real operating conditions by estimating optimal parameters is mandatory for many engineering applications, as it controls the energy management of the battery in a hybrid system. However, model parameters can vary according to the electrochemical nature of the battery, so improving the accuracy of the battery model parameters is essential to obtain reliable and accurate equivalent circuits. Therefore, this paper proposes a new efficient hybrid optimization approach for determining the proper parameters of Li-ion battery Shepherd model equivalent circuits. The proposed algorithm comprises a white shark optimizer (WSO) and the whale optimization approach (WOA) for modifying the stochastic behavior of the WSO while searching for food sources. Minimizing the root mean square error between the estimated and measured battery voltages is the objective function considered in this work. The hybrid variant of the WSO (HWSO) was examined with two different types of batteries. Moreover, the proposed HWSO was validated versus a set of recent meta-heuristic approaches including the sea horse optimizer (SHO), artificial gorilla troops optimizer (GTO), coyote optimization algorithm (COA), and the basic version of the WSO. Furthermore, statistical analyses, mean convergence, and fitting curves were conducted for the comparisons. The proposed HWSO succeeded in achieving the least fitness values of 2.6172 × 10⁻⁴ and 5.6118 × 10⁻⁵ with standard deviations of 9.3861 × 10⁻⁵ and 3.2854 × 10⁻⁴ for battery 1 and battery 2, respectively. On the other hand, the worst fitness values were 6.5230 × 10⁻² and 6.6197 × 10⁻⁵ via SHO and WSO for both considered batteries. The proposed HWSO results prove the efficiency of the proposed approach in providing highly accurate battery model parameters with high consistency and a unique convergence curve compared to the other methods. Full article

Knowledge of the 3-dimensional space use of large marine predators is central to our understanding of ecosystem dynamics and for the development of management recommendations. Horizontal movements of white sharks, Carcharodon carcharias, in eastern Australian and New Zealand waters have been relatively well studied, yet vertical habitat use is less well understood. We dual-tagged 27 immature white sharks with Pop-Up Satellite Archival Transmitting (PSAT) and acoustic tags in New South Wales coastal shelf waters. In addition, 19 of these individuals were also fitted with Smart Position or Temperature Transmitting (SPOT) tags. PSATs of 12 sharks provided useable data; four tags were recovered, providing highly detailed archival data recorded at 3-s intervals. Horizontal movements ranged from southern Queensland to southern Tasmania and New Zealand. Sharks made extensive use of the water column (0–632 m) and experienced a broad range of temperatures (7.8–28.9 °C). Archival records revealed pronounced diel-patterns in distinct fine-scale oscillatory behaviour, with sharks occupying relatively constant depths during the day and exhibiting pronounced yo-yo diving behaviour (vertical zig-zag swimming through the water column) during the night. Our findings provide valuable new insights into the 3-dimensional space use of Eastern Australasian (EA) white sharks and contribute to the growing body on the general ecology of immature white sharks. Full article

Statistical distribution approaches have been developed to describe wind data due to the intermittent and unpredictable nature of wind speed. The Weibull distribution with two parameters is thought to be the most accurate distribution for modeling wind data. This study seeks wind energy assessment via searching for the optimal estimation of the Weibull parameters. For this target, analytical and heuristic methods are investigated. The analytical methods involve the maximum likelihood, moment, energy pattern factor, and empirical methods, while the heuristic optimization algorithms include particle warm optimization and the Aquila optimizer (AO). Both analytical and heuristic methods are assessed together to fit the probability density function of wind data. In addition, nine models are submitted to find the most appropriate model to represent wind energy production. The error between actual and estimated wind energy density is computed to the model for each study site which has less error of energy. The fit test is performed with real data for the Zafarana and Shark El-Ouinate sites in Egypt for a year. Additionally, different indicators of fitness properties are assessed, such as the root mean square error, determination coefficient (R²), mean absolute error, and wind production deviation. The simulation results declare that the proposed AO optimization algorithm offers greater accuracy than several optimization algorithms in the literature for estimating the Weibull parameters. Furthermore, statistical analysis of the compared methods demonstrates the high stability of the AO algorithm. Thus, the proposed AO has greater accuracy and more stability in the obtained outcomes for Weibull parameters and wind energy calculations. Full article

To describe the growth pattern of the bonnethead shark (Sphyrna tiburo) in the Gulf of Mexico, a von Bertalanffy (VB) model has been automatically fit, which indicated a single−phase continuous growth without oscillations, though this would generate biases if this hypothesis is not confirmed. The objective of this research was to describe the growth pattern of S. tiburo under a multimodel approach based on information theory and contrasting single−phase models (VB, Gompertz, logistic models, and variants) and biphasic models (Soriano model and variants). The VB model was not supported. The Soriano model, with the variant in growth rate (k) and including length at birth (L₀), was selected with 100% supporting evidence. The hypothesis of the two−phase growth of S. tiburo with an increase in k, more than L_∞, fitted to L₀, is confirmed, and a correspondence was identified between growth−phase change sizes and the sizes reported in the literature for change in the juvenile–adult stages in females and for onset of reproductive maturity in males and both sexes. Full article

A wideband, low-profile, 3D automotive antenna for Long-Term Evolution (LTE) and 5G applications is presented in this paper. Different from other cellular antennas typically placed under the shark-fin cover or inside a car’s plastic spoiler, the proposed antenna is designed to be integrated inside the vehicle’s dashboard. The 35.5 × 40 × 45 mm³ antenna is compact, lightweight and robust. At the same time, this antenna is capable of operating from 670 up to 5000 MHz, covering the entire LTE/5G band (overall fractional bandwidth of 198%). A shunt stub was introduced between the monopole and ground plane to achieve a low LTE band and provide mechanical robustness for the proposed structure. Simulated performance in terms of reflection coefficient, radiation pattern and realized gain is described, showing a good agreement with the measurement results. Specifically, the antenna has a gain higher than −1 dBi at the low-frequency band (i.e., below 1 GHz) and higher than 3 dBi at the upper-frequency band (i.e., above 1.7 GHz). As per requirements, the ground plane size and layout can be properly chosen to fit the antenna into the available volume as well as to optimize the antenna’s performance. Full article

The global ocean has been shielding our planet from abrupt climate change by absorbing a large portion of the anthropogenically emitted carbon dioxide and the excess heat trapped in the atmosphere, leading to ocean acidification and warming. Additionally, oxygen loss in the ocean (also known as deoxygenation) is being exacerbated by rising global temperatures. This complex 3-way interaction (“deadly trio”) will definitely shape populations’ fitness and ecosystems’ health in the ocean of tomorrow. Here, I discussed the differential impacts of the “deadly trio” on the marine biota, with a special emphasis on sharks. Full article