Wolf Pack Research Articles

A Path-Planning Method Recommended for Multi-UAV Police Patrols Based on the Wolf Pack Optimization Algorithm Using CDRS and DRSS

Multi-UAV path planning for police patrols plays an important role in public security work, and while many path-planning algorithms have been applied in this area, all of them possess various degrees of shortcomings. To further improve the accuracy and efficiency of multi-UAV path planning for police patrols, this paper proposes a multi-UAV police patrol path-planning method based on an improved wolf pack optimization algorithm using the strategies of Composite Directional Raid and Dynamic Random Search (PMU-3PM-IWPA). Firstly, a multi-UAV police patrol path-planning model was constructed to reflect the planning problem for multi-UAV police patrol paths (PMU-3PM). Moreover, to enhance the performance of the existing wolf pack optimization algorithm, this paper proposes an improved wolf pack optimization algorithm (for short CDR-DRS-WPOA), including the Composite-Directional Raid Strategy (CDRS), aimed at enhancing global exploration capability, as well as the Dynamic Random Search Strategy (DRSS), with a view to speeding up the convergence for simple problems and heightening the optimization accuracy for difficult problems. Finally, the improved wolf pack optimization algorithm was adopted to solve the issue of multi-UAV path planning for police patrols, and numerical experiments were carried out on 20 public classical datasets as well as PMU-3PM compared with GA, PSO, WDX-WPOA, and DAF-BRS-CWOA. The results indicate that CDR-DRS-WPOA took 20~80% less time and possessed more optimization accuracy, and that PMU-3PM-IWPA based on CDR-DRS-WPOA offers excellent performance.

A new method for recognizing geometric parameters of industrial robots

Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first apply the D-H method to establish a model of error in industrial robots, and then use the set of errors in their geometric parameters as the objective function. Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. We balance the convergence of the PSO algorithm by using a linearly diminishing weight. This leads to an improved PSO algorithm that can accurately determine errors in the geometric parameters of industrial robots. We compared our improve PSO algorithm with commonly used particle swarm algorithms, and the results showed that the former had a higher accuracy of convergence on average. Moreover, the errors in the geometric parameters obtained by the improved PSO algorithm can enhance the accuracy of localization of errors in industrial robots.

Plural breeding in Gray Wolf (<i>Canis lupus</i>) packs: how often?

The occurrence of more than a single female breeder in North American Gray Wolf (Canis lupus) packs, i.e., plural breeding, is well known, but its incidence has not been estimated since 1982. Using winter pack size as an index to plural breeding inwolves, I reviewed the literature from North American populations least exploited by humans to assess the general incidence of plural breeding. Generally winter packs >15 were associated with incidents of plural breeding. Wolf packs preying primarily on White-tailed Deer (Odocoileus virginianus) and in locations south of 52°N latitude seldom exceeded 10–15. Plural breeding occurred in packs preying primarily on larger ungulates in areas mostly above 52°N. The estimated incidence of plural breeding in the overall wolf population was <15% and perhaps <10%, which is lower than a 1982 estimate of at least 20–40%. I discuss reasons why plural breeding is associated with larger prey.

Использование алгоритмов роевого интеллекта для определения состава мультипроекта

One of the promising ways to reduce the dependence of domestic industry on the supply of critical goods, components and raw materials necessary for the construction and effective functioning of multi-stage production and technological chains is to intensify processes for their import substitution, including through various measures of state support. However, the critical need for a wide range of products requires the selection of the most “promising” projects for inclusion in program-target documents using a set of criteria (sometimes even non-financial). As a result, there arises an urgent scientific and practical task of developing approaches to the formation of multi-projects (a set of projects) that can qualify for state support under various programs to reduce import dependence and overcome the technological backwardness of Russian industry, based on the use of modern economic and mathematical methods. In the application to this task, a multi-project can be represented as a “set” (unrelated projects), a “chain” (rigid sequence of projects) or a “network” (projects with complex logical-temporal interrelations). The specifics of each type determine the conditions and impose restrictions on the processes of selecting components for inclusion in their composition, which consists of finding the best combination of projects and/or programs, i. e., it is reduced to a task of conditional multidimensional optimization. In the absence of a requirement to find a “strictly optimal” composition, one can use metaheuristic methods that are capable of finding solutions close to these in an “acceptable” time. Among them, the largest and most well-known class are swarm intelligence algorithms based on the principles of collective behavior of a population of living organisms. To form the composition of multi-projects, the article proposes to use algorithms inspired by the collective behavior of a pack of wolves (Grey Wolf Optimizer) and a school of fish (Fish School Search) to satisfy their food needs. To increase the efficiency of their use for solving the task of finding the best composition of a “set” and “network” of projects, their hybridization with fuzzy logic methods (in particular, fuzzy clustering and fuzzy-logical inference) was proposed.

Parameters identification of magnetorheological damper based on improved grey wolf optimization algorithm

Abstract Parameter identification of the mechanical model of magnetorheological dampers is usually carried out through a combination of optimization algorithms, but there is little research on the impact of the optimization algorithm itself on the identification results. In order to improve the accuracy of parameter identification results, the influence of the parameters of the optimization algorithm itself on the fitting results was investigated in this study, and the optimization algorithm adopted the improved grey wolf algorithm. The effects of different wolf pack numbers and iteration times on recognition results were examined in this study. The various parameters in the mechanical model of magnetorheological dampers are determined utilizing the optimal combination of algorithm parameters. Finally, the validity of the identification results was verified by evaluating the consistency between the identified damping force and the experimental damping force. The results indicate that when the optimal combination is used, the accuracy of parameter identification can be improved.

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

A framework for multi-UAV task allocation and path planning in complex urban environments

Abstract The complexity of urban combat environments, the coupling of task allocation and path planning, and the existence of dynamic targets significantly increased the complexity of coordinated UAV swarm attack tasks. In this work, we proposed a multi-UAV task allocation and path planning framework inspired by the collaborative hunting behaviour of wolf packs. This framework was based on a multi-target k-winner-take-all (k-WTA) algorithm and an improved grey wolf optimization (GWO) algorithm. Firstly, for the multi-task allocation problem in unknown environments, a multi-objective k-WTA algorithm was used for task allocation based on the competition mechanism, which realized fast task allocation in dynamic environments. Then, the advantages of GWO and genetic algorithm (GA) were combined through GA-WPO to overcome the random initialization problem of GWO by using GA as an initialization generator. A path planner based on GA-WPO was proposed to enable multi-UAVs to reach the target point safely in complex urban environments. Finally, the proposed path planner was used for multi-UAV path planning, and the effectiveness of the method was verified by a set of simulation experiments, which showed that the method better solved the coupling problem of path planning and task assignment for UAV swarm coordinated attack tasks.

Concurrent Rabies and Canine Distemper Outbreaks and Infection in Endangered Ethiopian Wolves.

Intensive disease surveillance in an endangered population of Ethiopian wolves provided evidence of concurrent outbreaks of rabies and canine distemper viruses in 2019, including co-infection in an individual animal. Disease surveillance and intensive monitoring of wolf packs in Ethiopia were essential in detecting the concurrent outbreaks and enabled accurate assessment of disease from both pathogens. The study highlights the risk posed to endangered populations that are susceptible to, or live in areas with, reservoir hosts for canine distemper and rabies viruses. Instances of concurrent distemper and rabies outbreaks appear unusual in the existing literature; modeling for one disease might underestimate the risk for extinction. Concurrent outbreaks may have a larger effect than single-disease outbreaks, even in a population that has partial vaccination coverage. Researchers studying wildlife populations from a conservation perspective should be aware that both diseases can strike at once where susceptible populations exist.

Spiralizer Strategy with Wolf Pack Algorithm and Temporal Convolution Network – Gated Recurrent Unit for Coconut Crop Yield Prediction

Spiralizer Strategy with Wolf Pack Algorithm and Temporal Convolution Network – Gated Recurrent Unit for Coconut Crop Yield Prediction

Application of the Gray Wolf Optimization Algorithm and Neural Networks for Solving Discrete Problems

Relevance. In recent decades, metaheuristic optimization methods have become popular for solving complex problems that require searching for global extrema. Algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), as well as more modern approaches such as cat pack optimization (CSO) and gray wolf pack optimization (GWO) demonstrate high efficiency, but their application is often limited by the conditions of continuity and differentiability of the objective functions. This is a challenge when solving problems with discrete data, where such requirements are not met. In this context, the search for methods that allow adapting metaheuristic algorithms to work with discrete functions is of particular relevance.Aim. The study is aimed at testing the hypothesis about the possibility of using a neural network trained on a limited set of discrete data as an approximation of a function sufficient for the correct execution of the GWO algorithm when searching for a global minimum. The implementation of this hypothesis can significantly expand the scope of GWO, making it available for a wider range of problems where functions are defined on discrete sets.Methods. The study is based on the analysis of existing approaches and experimental verification of the hypothesis on two test functions: a linear function and a Booth function, which are widely used as standards for evaluating the performance of optimization algorithms. Numerical experiments were conducted using neural networks as an approximating model to obtain the results. Solution. During the experiments, an analysis of the applicability of neural networks for approximating discrete functions was carried out, which showed the success of this approach. It was found that neural networks can approximate discrete functions with high accuracy, creating conditions for a successful search for a global minimum using the GWO algorithm.Novelty. For the first time, a hypothesis was proposed and tested on the use of neural networks for approximating objective functions in metaheuristic optimization problems on discrete data. This direction has not previously received due coverage in the scientific literature, which adds significance to the obtained results and confirms the effectiveness of the proposed approach.Practical significance. The results of the study open up new prospects for the application of algorithms such as GWO in optimization problems based on discrete data, expanding the capabilities of metaheuristic methods and facilitating their implementation in a wider class of applied problems, including problems where the use of other methods is limited.

The Social Memory of Sexual Violence: "You Touch One, You Touch Us All".

The social imaginary that legitimizes sexual violence is continuously reinforced by discourses that are deployed in the aftermath of physical attacks. These, in turn, nourish a specific type of collective memory from which clear social identities emerge. This article identifies the textual trajectories of social meanings associated with sexual violence and their discursive expressions in media, legal, and political discourses. The examples provided here are related to the Spanish "wolf pack case" and its social and political consequences. It concludes that the case not only generated a conceptual battle about violence but also a mnemonic dispute related to newly interpellated subjectivities.

Application of intelligent algorithms in optimal operation of pumping stations

Abstract An optimal operation model of pumping stations is established in this paper, with the minimum energy consumption of the pumping station as the optimization objective, considering the constraints of the pumping station operating head, flowrate of the pumping station and so on. Simulated annealing is introduced into genetic algorithm, particle swarm optimization, wolf pack algorithm and grey wolf optimization to solve the model. The minimum value, average value, relative standard deviation of the objective function and infeasibility of constraint conditions are selected as the evaluation indexes of the algorithms performance. The calculation results show that simulated annealing ensures that the intelligent algorithms can find the optimal solution in the feasible solution space and avoid them falling into local optimization. The operation schemes obtained by solving the example model with the improved algorithm can reduce the energy consumption of the pumping station by 5.759%∼6.682%, which provides guidance for the operation schemes of the example pumping station. The results can be further applied in other pumping stations.

A hybrid artificial bee colony algorithm with high robustness for the multiple traveling salesman problem with multiple depots

A hybrid artificial bee colony algorithm (AC-ABC) with high robustness is proposed to solve the multiple traveling salesman problem (MTSP) with multiple depots. It initially conducts small-scale local searches to generate a high-quality population. Subsequently, a probabilistic model is established to balance global and local searches in the process of updating this population and exploring the optimal solution for the MTSP based on pheromone concentration and city visibility. In the process of population representation and updating, we introduce a novel tensor representation, which not only offers more opportunities for crossover between populations, but also adaptively provides more route choices to meet the personalized needs of salesmen. Besides artificial bee colony (ABC), AC-ABC takes at least 23% less execution time than other algorithms to solve the MTSP on multiple TSPLIB instances, especially takes about 32%–93% less execution time than the ant colony-partheno genetic algorithm (AC-PGA). The travel cost of the optimal route obtained by AC-ABC is significantly better than that of ABC, partheno genetic algorithm (PGA), improved PGA (IPGA), and two-part wolf pack search (TWPS). AC-ABC always obtains less travel cost than AC-PGA when the number of cities n≤150. AC-ABC only obtains about 0.5%–7.4% more travel cost than AC-PGA when the number of cities n>150.

An Elite Wolf Pack Algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction’s efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.

Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation.

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.

Optimization of personnel evacuation routes for sudden nuclear accidents based on hybrid genetic-grey wolf optimizer algorithm

To address the emergency situation resulting from a sudden nuclear accident, it is imperative to develop an optimal emergency response plan that ensures the prompt and safe evacuation of affected individuals from the hazardous area.The paper proposed the hybrid genetic-grey wolf optimizer algorithm (GA-GWO).This algorithm integrated radiation environment and road network to plan evacuation paths that minimizes the radiation dose. This algorithm enhanced the convergence and search capabilities of the genetic algorithm (GA) by incorporating wolf pack characteristics and internal competitive behavior. This improvement allowed for more efficient exploration of potential evacuation routes. The feasibility of GA-GWO was validated through a visualization simulation utilizing Web Geographic Information Systems (WebGIS). The findings indicated that GA-GWO generated evacuation paths that minimize radiation dose, providing safe routes for the affected population. This approach demonstrates significant potential for application in emergency response planning for sudden nuclear accidents, ensuring both prompt and safe evacuations.

‘With you, sister @JenniHermoso’: Productive rage in the viralization of #SeAcabó

The hashtag #SeAcabó [It’s over], which was originally published on X by Spanish football player Alexia Putellas in support of her teammate Jenni Hermoso following a nonconsensual kiss by the then president of Spanish football, Luis Rubiales, managed to channel the transformative collective rage toward sexual violence in Spain. Following the World Cup final, while mainstream media, especially sports media, either ignored the act, gave Rubiales a voice, or even defended him, indignation at an act seen as sexual violence went viral on the networks. #SeAcabó spurred the expression of rage in a digital environment conditioned by the previous La Manada ( The Wolf Pack) gang rape case in 2016 that gave rise to the hashtag #YoSiTeCreo [I do believe you] in support of the accuser. While Rubiales has maintained that the kiss was consensual and has accused ‘false feminism’ of conspiring against him, Hermoso has become a legitimate spokesperson for those who fight against the affective injustice suffered by victims of sexual violence, who even go so far as to deny their condition, for fear of not being believed. In this short piece, we draw on Lorde and Cherry’s concept of productive rage to argue that Spanish feminism fostered, through the #SeAcabó movement, collective resistance to sexual violence, managing to put consent at the center of the debate in the public sphere. This piece is a part of a special themed issue on the Rubiales / Hermoso non-consensual kiss.

Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm

Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.

ERRT-GA: Expert Genetic Algorithm with Rapidly Exploring Random Tree Initialization for Multi-UAV Path Planning

In unmanned aerial vehicle (UAV) path planning, evolutionary algorithms are commonly used due to their ability to handle high-dimensional spaces and wide generality. However, traditional evolutionary algorithms have difficulty with population initialization and may fall into local optima. This paper proposes an improved genetic algorithm (GA) based on expert strategies, including a novel rapidly exploring random tree (RRT) initialization algorithm and a cross-variation process based on expert guidance and the wolf pack search algorithm. Experimental results on baseline functions in different scenarios show that the proposed RRT initialization algorithm improves convergence speed and computing time for most evolutionary algorithms. The expert guidance strategy helps algorithms jump out of local optima and achieve suboptimal solutions that should have converged. The ERRT-GA is tested for task assignment, path planning, and multi-UAV conflict detection, and it shows faster convergence, better scalability to high-dimensional spaces, and a significant reduction in task computing time compared to other evolutionary algorithms. The proposed algorithm outperforms most other methods and shows great potential for UAV path planning problems.

Retraction Note: Research on reliability of sports intelligent training system based on hybrid wolf pack algorithm and IoT

Retraction Note: Research on reliability of sports intelligent training system based on hybrid wolf pack algorithm and IoT