Search Results (22)

The Diminishing-Return (DR)-submodular function maximization problem has garnered significant attention across various domains in recent years. Classic methods often employ continuous greedy or Frank–Wolfe approaches to tackle this problem; however, high iteration and subproblem solver complexity are typically required to control the approximation ratio effectively. In this paper, we introduce a strategy that employs a binary search to find the dynamic stepsize, integrating it into traditional algorithm frameworks to address problems with different constraint types. We demonstrate that algorithms using this dynamic stepsize strategy can achieve comparable approximation ratios to those using a fixed stepsize strategy. In the monotone case, the iteration complexity is $O\left({\parallel \nabla F\left(\mathbf{0}\right)\parallel }_1{ϵ}^{-1}\right)$, while in the non-monotone scenario, it is $O\left(n+{\parallel \nabla F\left(\mathbf{0}\right)\parallel }_1{ϵ}^{-1}\right)$, where F denotes the objective function. We then apply this strategy to solving stochastic DR-submodular function maximization problems, obtaining corresponding iteration complexity results in a high-probability form. Furthermore, theoretical examples as well as numerical experiments validate that this stepsize selection strategy outperforms the fixed stepsize strategy. Full article

A grid-tied inverter needs excellent maximum power point tracking (MPPT) topology to extract the maximum energy from PV panels regarding energy creation. An efficient MPPT ensures that grid codes are met, maintains power quality and system reliability, minimizes power losses, and suppresses rapid response to power fluctuations due to solar irradiance. Moreover, appropriate MPPT enhances economic returns by increasing energy royalties and ensures high power quality with reduced harmonic distortion. For these reasons, an improved hybrid MPPT technique for a grid-tied solar system is presented based on particle swarm optimization (PSO) and grey wolf optimizer (GWO-PSO) to achieve these objectives. The proposed method is tested under MATLAB/Simulink 2024a for a 100 kW PV array connected with a boost converter to link with a voltage source converter (VSC). The simulation results show that the proposed GWO-PSO can reduce the overshoot on rise time along with settling time, meaning less time is wasted within the grid power system. Moreover, the suggested method is compared with PSO, GWO, and horse herd optimization (HHO) under different weather conditions. The results show that the other algorithms respond more slowly and exhibit higher overshoot, which can be counterproductive. These comparisons validate the proposed method as more accurate, demonstrating that it can enhance the real power quality that is transferred to the grid. Full article

Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. The methodology constructs a physics-informed objective function linking process parameters to optimization targets, thereby resolving the disconnect between mechanistic and data-driven modeling approaches. The algorithm innovatively combines Sobol quasi-random sequences with grey wolf social hierarchy strategies to prevent premature convergence in high-dimensional search spaces while maintaining Pareto front diversity, supplemented by a reward mechanism to ensure strict adherence to multi-objective constraints. Experimental validation using steel plant production data demonstrates IMOWOA’s efficacy, achieving a 10.8% reduction in endpoint phosphorus content and a 5.79% decrease in production costs per ton of steel. Comparative analyses further confirm its superior feasibility and stability in quality-cost co-optimization, evidenced by a 12.6% improvement in hypervolume (HV) over conventional swarm intelligence benchmarks, establishing a robust framework for industrial metallurgical process optimization. Full article

Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance. Full article

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset. Full article

The construction and development of energy storage are crucial areas in the reform of China’s power system. However, one of the key issues hindering energy storage investments is the ambiguity of revenue sources and the inaccurate estimation of returns. In order to facilitate investors’ understanding of revenue sources and returns on investment of energy storage in the existing electricity market, this study has established multiple relevant revenue quantification models. The research methodology employed in this paper consists of three main components: Firstly, we established a revenue model and a cost model for energy storage participation in the electricity market. These models focus on arbitrage revenue, subsidy revenue, auxiliary services revenue, investment cost, operational and maintenance cost, and auxiliary service cost of energy storage. Subsequently, we utilized an enhanced Grey Wolf Optimizer algorithm to solve the optimization problem and maximize revenue, thus obtaining the optimal capacity and revenue scale of energy storage in the electricity market. Finally, we compared the whole-lifecycle ROI of different energy storage options in various scenarios. The evaluation results demonstrate that the difference between peak and off-peak loads impacts the investment demand and charging/discharging depth of energy storage. In addition, the discrepancy between peak and off-peak prices affects the arbitrage return of energy storage. These two factors can serve as criteria for energy storage investors to assess their return expectations. When solely considering economic returns and disregarding technical factors, pumped storage energy storage emerges as the most suitable mechanical energy storage option requiring investment. The main contribution of this study lies in the estimation of the lifecycle investment returns for various energy storage technologies in the Chinese electricity market, thus providing valuable insights for the investment and operational practices of market participants. Full article

Renewable energy systems have the potential to address increasing energy demand, mitigate environmental degradation, and decrease reliance on fossil fuels. Wind and solar power are examples of renewable energy sources that are characterized by their cleanliness, environmental friendliness, and sustainability. The combination of wind and solar energy is motivated by each energy source’s inherent variability. The objective of this study is to assess the technical, economic, and environmental aspects of a hybrid system designed to provide energy. This study utilizes numerical simulation and develops a novel model using the gray wolf optimization (GWO) algorithm to assess the technical, economic, and environmental consequences of adopting a hybrid system. The evaluation focused on determining the optimal configuration of a greenhouse unit in Najran, Saudi Arabia, over a period of 20 years. The results showed that the diesel generator produced 42% of the required energy when combined with photovoltaic generators, while photovoltaics produced 58%. The wind turbine generated 23% of the required power while the remaining 77% was produced by the diesel generator. Finally, diesel generators, photovoltaics, wind turbines were observed to generate 37%, 48%, and 15% of the required energy, respectively. This outcome is consistent with current knowledge because solar and wind systems reduce pollution. However, the diesel generator–photovoltaic–wind mode is the preferred method of reducing emissions. Finally, the rate of return on investment for diesel generators is 3.4 years, while for diesel-photovoltaic generators and the triple array it is 2.5 and 2.65 years, respectively. Full article

Summer pasture grazing is perceived as being under pressure from renewed wolf presence in the Italian Alpine province of South Tyrol. To investigate this, we combined a literature review with expert interviews to (1) get an overview of the situation of small ruminant farming and (2) identify drivers with an influence on summer pasture use, including wolf presence. Firstly, the results show an increase in small ruminant farms and stock numbers in the valley and on summer pastures during the last years. Secondly, subsidy programs, off-farm employment, lack of personnel and tourism are some of the main drivers for summer pasture development in the past and future. Thirdly, this analysis detects the potential pressure of wolf presence on small ruminant summer farming. Nevertheless, this impact should still be considered modest compared to other driving processes that have started before the return of the wolves. Based on this, the study finally discusses the importance of focusing on targeted funding, the revalorization of the shepherd profession and the balance between tourism and livestock farming to support summer pasture farming in the future. Full article

Trichinella is a foodborne parasite whose wildlife reservoirs are represented by carnivores and omnivores with predatory and scavenger behavior. The aim of the present study was to investigate the occurrence of Trichinella infection in grey wolves (Canis lupus) that recolonized the Western Alps from the end of the past century, and discuss the epidemiological role played by this apex predator in the early phases of its return. During the period 2017–2022, diaphragm samples were obtained from 130 individuals collected in the frame of a wolf mortality survey. Trichinella larvae were found in 15 wolves (11.53%) with a parasite intensity of 11.74 larvae per gram. Trichinella britovi was the only species identified. This is the first prevalence survey of Trichinella in wolves recolonizing the Alps. Results suggest that, in this particular biotope, the wolf has rejoined the Trichinella cycle and has the potential to play an increasingly important role as maintenance host. Arguments in favor and against this perspective are discussed and knowledge gaps highlighted. The calculated Trichinella larval biomass in the estimated wolf population roaming in Northwest Italy will serve as baseline value to explore possible shifts in the relative importance of wolves as Trichinella reservoir within the regional carnivore community. Finally, wolves re-colonizing the Alps already appear as sensitive sentinels to monitor the risk of Trichinella zoonotic transmission by infected wild boar meat. Full article

This article introduces a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has the biology of the plum trees as its main source of inspiration. The PTA was tested and validated using 24 benchmark objective functions, and it was further applied and compared to the following selection of representative state-of-the-art, nature-inspired algorithms: the Chicken Swarm Optimization (CSO) algorithm, the Particle Swarm Optimization (PSO) algorithm, the Grey Wolf Optimizer (GWO), the Cuckoo Search (CS) algorithm, the Crow Search Algorithm (CSA), and the Horse Optimization Algorithm (HOA). The results obtained with the PTA are comparable to the results obtained by using the other nature-inspired optimization algorithms. The PTA returned the best overall results for the 24 objective functions tested. This article presents the application of the PTA for weight optimization for an ensemble of four machine learning regressors, namely, the Random Forest Regressor (RFR), the Gradient Boosting Regressor (GBR), the AdaBoost Regressor (AdaBoost), and the Extra Trees Regressor (ETR), which are used for the prediction of the heating load and cooling load requirements of buildings, using the Energy Efficiency Dataset from UCI Machine Learning as experimental support. The PTA optimized ensemble-returned results such as those returned by the ensembles optimized with the GWO, the CS, and the CSA. Full article

Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R² index. Full article

As a consequence of the exponential increase of the demographic and technological development of the human being, conflicts with the natural environment are accentuated. Pollution or the loss of biodiversity represent examples of problems that we must face to maintain the balance between the evolution of human beings and the conservation of nature. However, there are conflicts whose origin is not as modern as those mentioned, and we return to the Neolithic to find the origin of the conflict of man with the great predators. This condition has existed since then and at this point in history, is reaching very high levels of tension in developed countries, as a result of the depredation of livestock. Wolf is one of the species that generates more conflict and is currently suffering a slight demographic expansion. Although current laws mostly seek their recovery and conservation, the wolf is experiencing great difficulties due to the poor social perception it has. Faced with this situation, a model has been developed using geographic information systems which categorizes the areas according to their probability that the cattle could suffer a wolf attack. Based on natural and anthropogenic variables of the environment, the areas with a greater or lesser probability of attack were evaluated, with the objective of designing a prevention plan to reduce or eliminate the attacks. Since different authors demonstrate that population control measures on the species are not effective in reducing attacks on livestock, the solution to the conflict should be based on preventive measures. The use of the designed model will enable the competent authorities to apply these measures optimally, reducing expenses and allowing to anticipate future areas of conflict. Full article

The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively. Full article

A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Lévy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA). Full article

Our work shows that, despite the persistence of persecutory actions, conservation activity has proved successful for the return of numerous wild mammals to different habitats, including the wolf. The human-wolf conflict is still described in all countries where the wolf is present. This is evidenced by the high number of damages on livestock, and the corpses of wolves found both in protected areas and in those where hunting is permitted. The diagnosis of road accidents, together with poisoning and poaching, are major causes of mortality. Although hunting records the highest percentage of kills in Spain, the demographic stability reported by the censuses suggests that this activity does not have a consistent influence on the Iberian wolf population’s survival. In Italy, where wolf hunting is prohibited, wolf populations are to be increasing. In some Italian situations, wolf attacks on horses seem to cause unwanted damage to foals, but they represent a very precious source of information about the habits of carnivores. A simple management plan would be sufficient to help the coexistence between the productive parts and the ecosystem services ensured by the presence of the wolf. The presence of hybrids is a negative factor. Full article