Behavior Of Wolves Research Articles

An Improved grey wolf optimizer with weighting functions and its application to Unmanned Aerial Vehicles path planning

The grey wolf optimizer (GWO) is an optimization algorithm that draws inspiration from nature. It is an optimization algorithm based on population that iteratively searches for the optimal solution by simulating the social behavior and hunting behavior of grey wolves. It has recently been shown that GWO can be improved by the introduction of initializing, movement, selecting and updating. In this paper, we extend an improved grey wolf optimizer with weighting functions (IGWO-WFs), which include multi-modal adaptive function, sigmoid function and autoregressive function. The IGWO-WFs has 74% improved to the conventional algorithms. It can be resolved the instability and convergence issues of GWO and investigate the effectiveness of the methods through numerical simulations and the path planning of Unmanned Aerial Vehicles (UAVs).

BEHAVIORAL RESPONSES OF CAPTIVE MANED WOLVES TO OLFACTORY ENRICHMENT

Environmental enrichment (EE) is a technique that increases animal welfare. The behavior of maned wolves when exposed to four odors hidden outside the enclosure was investigated. Using the focal animal method and recording all occurrences, their behavior was analyzed for five minutes in each baseline phase (Basal), during exposure (Exp), and after withdrawal of the stimulus (Pos). Behavioral responses were categorized as positive (P+), negative (N-), and other (Ot). The average P+ increased significantly (P < 0.05) from the Basal to the Exp, but it was not statistically different (P = 0.60) between the Basal and the Pos. Neither N- nor Ot changed statistically in any of the phases (P > 0.5). Maned wolves appeared to be sensitive to this EE with the olfactory stimuli used during the period of olfactory stimulation. The method used is practical, safe, and flexible, and can be useful in sensory enrichment programs.

Flexible Job Shop Scheduling Optimization for Green Manufacturing Based on Improved Multi-Objective Wolf Pack Algorithm

Green manufacturing has become a new production mode for the development and operation of modern and future manufacturing industries. The flexible job shop scheduling problem (FJSP), as one of the key core problems in the field of green manufacturing process planning, has become a hot topic and a difficult issue in manufacturing production research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed for solving a multi-objective flexible job shop scheduling problem with transportation constraints. Firstly, a multi-objective flexible job shop scheduling model with transportation constraints is established, which takes the maximum completion time and total energy consumption as the optimization objectives. Secondly, an improved wolf pack algorithm is proposed, which designs individual codes from two levels of process and machine. The precedence operation crossover (POX) operation is used to improve the intelligent behavior of wolves, and the optimal Pareto solution set is obtained by introducing non-dominated congestion ranking. Thirdly, the Pareto solution set is selected using the gray relational decision analysis method and analytic hierarchy process to obtain the optimal scheduling scheme. Finally, the proposed algorithm is compared with other algorithms through a variety of standard examples. The analysis results show that the improved multi-objective wolf pack algorithm is superior to other algorithms in terms of solving speed and convergence performance of the Pareto solution, which shows that the proposed algorithm has advantages when solving FJSPs.

Silencing the call of the wild – howling behaviour and responses of the wolf to Anthropocene in India

AbstractWolves use howls to maintain large territories, intra‐pack communication and social bonding. Besides their physical presence, howls are also instrumental in creating fear and impacting foraging behaviour among the lower cascade. Anthropocene‐led behavioural alteration in vocalization has been observed in a wide range of species, but the effect on wolf howl is unknown. In this context, we have studied the howling behaviour of the Indian wolf through playback surveys (n = 264) across the anthropogenic gradient. We found a disparity in their howl response – based on the distance to villages. In the low disturbed East‐Maharashtra (EM), wolves mostly avoided responding to howling surveys (HS) if done within 1200 m of villages [response rate (RR) = 0.03 ± 0.021], but they did respond once it was done far from villages (>1200 m) (RR = 0.226 ± 0.075). In high human‐density West‐Maharashtra (WM), wolves showed high RR within 1200 m from the villages (RR = 0.148 ± 0.031). But the RR within 500 m from villages was less as howling near villages might lead to easy detection. The collared wolf data showed significantly high RR (0.635 ± 0.067) in their home‐range core, but low RR if the core area was close to a village. Therefore, howling too close to a village is disadvantageous, although their tolerance for responding to HS has increased in the human‐dominated landscape. The extent of the village may increase further with development, which will leave fewer areas for the wolf to defend territory with a long‐range howl. The wolves might behaviourally adapt to a human‐modified landscape by reducing their howling intensity. Adaptation to a fragmented habitat may save the wolves from extinction, but the repercussions of the fundamental behavioural alteration might adversely impact wolf behaviour and the ecological cascade. Whereas ecologists are mainly concerned with the extinction of species, this study highlights the vulnerability of fundamental behaviour of a keystone species attributed to human‐induced contemporary evolution.

A report of short-term aversive conditioning on a wolf documented through telemetry

Predation by large predators on livestock is one of the main concerns in species conservation as it elicits prompt and effective retaliations. Therefore, conflict mitigation is essential to ensure long-term coexistence of predators with humans. We performed aversive conditioning (AC) with rubber bullets on one collared wolf that had become particularly bold toward a transhumant shepherd and had preyed on livestock. By exploiting the unique fine-resolution location data available before and after the AC event, alongside careful retrospective field investigations, we were able to analyse the effects of AC on wolf behaviour. Our study revealed that after just a single AC event, the wolf modified its spatial and predatory behaviour: the wolf changed its use of space by increasing distance from humans and ceased to attack farms in the following 2 months; actually, the only livestock preyed after AC was represented by a sheep and two goats lost by shepherds that had left alpine pastures. This study represents a first step to increase knowledge on AC effect on the wolf. Additional researchers are encouraged to conduct and publish findings on this topic in order to provide a useful and widely tested array of tools to promote wolf conservation in human-dominated landscapes.

Solving Economic Load Dispatch Problem Using Grey Wolf Optimization Algorithm

Abstract: This article presents a new evolutionary optimization approach named grey wolf optimization (GWO), which is based on the behavior of grey wolves, for the optimal operating strategy of economic load dispatch (ELD). Nonlinear characteristics of generators like ramp rate limits, valve point discontinuities and prohibited operating zones are considered in the problem. GWO method does not require any information about the gradient of the objective function, while searching for an optimum solution. The GWO algorithm concept appears to be a robust and reliable optimization algorithm is applied to the nonlinear ELD problems. The proposed algorithm is implemented and tested on two test systems having 40 Thermal generators. The results confirm the potential and effectiveness of the proposed algorithm compared to various other methods available in the literature. The outcome is very encouraging and proves that the GWO is a very effective optimization technique for solving various ELD problems.

Bad Wolves? Political Ecology of Responsibility and More-Than-Human Perspectives in Human–Wildlife Interactions

Given the widespread failure of anthropocentric approaches to wildlife conservation, questions of conviviality have become increasingly important for conservation efforts. We propose that political-ecological conceptualizations of other-than-human perspectives offer promising avenues for fostering more just and sustainable human-wildlife interactions. To explore these issues, we investigate wolf conservation in northeastern Finland, focusing on the contested coexistence of humans and wolves. Our study draws on data obtained through interviews and participant observation with local residents, interviews with wolf behavior researchers, and analysis of policy documents. Our findings highlight the fundamental roles of power and responsibility in human-wildlife coexistence, as well as the importance of attending to wolves’ intrinsic patterns of behavior. We argue for the value of distinguishing between human agency and other-than-human actions, as attributing intentional agency to wolves can obscure important aspects of human responsibility, political decision-making, and power dynamics at the intersections of humans and other-than-humans.

A preliminary investigation to establish conceptual behavior in gray wolves (Canis lupus).

Conceptual behavior represents a type of complex stimulus control where an organism differentially responds to examples and nonexamples of instances within a stimulus class. Different species have demonstrated conceptual behavior both in their natural environments and through experimental investigations. The current paper investigates preliminary methods to teach conceptual behavior to gray wolves (Canis lupus). The researchers used a match-to-sample arrangement to teach three shapes: a triangle, square, and cross varying in size, color, and positions. Probe trials used a novel set of stimuli to test for the emergence of conceptual behavior. Although the wolves did not show an immediate transfer to novel stimuli following initial match-to-sample training, they did show improvement after explicit discrimination training. We discuss the implications of these results as well as future methods that may enhance experimental procedures investigating concept learning in canids.

Machine learning-assisted tri-objective optimization inspired by grey wolf behavior of an enhanced SOFC-based system for power and freshwater production

In recent years paying attention to the generation of clean and sustainable power and fresh water along with having lower cost and emission has increased. In the present research, a novel scheme for generating efficient power using the flame-assisted fuel cell is introduced, which has higher efficiency than ordinary fuel cells due to increased hydrogen concentration in the flame-rich combustion chamber. The waste heat is then introduced to a multi-effect desalination unit through a heat recovery steam generation unit to generate fresh, drinkable water. In order to make the system have higher efficiency, lower cost, and lower emission, the machine learning techniques are applied to optimize the operational conditions of the system, and find out the best solution point based on the cutting-edge algorithm of the grey wolf. Also, a complete techno-economic analysis and a parametric study are necessary to figure out the best solution point based on the TOPSIS method. The results indicate that the maximum value of exergy efficiency and drinkable water generation is 67.5% and 3.4 kg/s, respectively, while the minimum energy cost is 90.1 $/MWh. Moreover, results show that for the second optimization scenario considering the drinkable water production, energy cost, and pollution index as the objectives, the net produced power, energy efficiency, exergy efficiency, and water mass flowrate improve by around 1059 kW, 5.1%, 1.3%, and 1.6 kg/s than the design condition. Besides, energy cost and emission index are reduced by about 22 $/MWh and 51.9 kg/MWh, respectively.

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

A Hybrid Grey Wolf Optimization Algorithm Using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Vale-Point Effect

This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature.

Managing Wolves is Managing Narratives: Views of Wolves and Nature Shape People’s Proposals for Navigating Human-Wolf Relations

The resurgence of wolf populations in Germany is causing controversies regarding their management policies. Through 41 semi-structured interviews with stakeholders, we found that respondents favored the management directives predicated on the narratives they entertained, i.e., beliefs about wolves and nature more broadly. We identified 18 narratives that ranged from the extreme of “beings-focused, harmony-oriented, and wolf-favoring” extreme through “ecosystem-focused, conservation-oriented, and wolf-ambivalent” to another extreme of “human-centered, dominion-oriented, and wolf-critical” extreme. The 24 directives aim to allow, balance, and control wolf behavior. Narratives and directives correlate: participants and stakeholders holding beings-focused views tend to propose more allowing directives, those endorsing ecosystem-focused perspectives lean to choose balancing directives, and those inclined to human-focused stances prefer controlling directives. Thus, our research allows wildlife managers to understand better why people endorse or oppose specific management options and devise effective communication strategies by working with the underlying narratives.

The development of object play in wolf puppies (Canis lupus)

ABSTRACT Studies exploring the development of object play of wolf pups are lacking. Comparisons of wolves vs. dogs can aid in teasing out the influences of domestication on any differences uncovered between wolves and various dog breeds. We investigated the development of object play from 2 to 9 weeks of age (14–63 days) in 2 litters of hand-reared wolf pups (Canis lupus). Both litters of pups were video-recorded in their home enclosures at week and mid-week timepoints. Each litter was given a separate set of the same five commercial dog (Canis lupus familiaris) toys with differing sensory and functional qualities. The first 10 min of activity from each session were analyzed using the Noldus Observer XT program and a previously developed ethogram of object related behaviors. The results were analyzed using repeated measures LMMs. Percent of time spent playing with toys, behavioral counts and behavioral diversity all increased significantly with age for wolf pups, and they were generally developmentally ahead of dog puppies. Wolves, like dogs, had preferences for some toys. These findings expand our grasp of infant wolf behavior, the comparative development of object play in canines, and provide a model for the study of domestication on play behavior.

Diet Analysis of the African Golden Wolf Canis anthus (Cuvier, 1820) in Tizi-ouzou, Kabylia Region (Algeria)

The diet of the African golden wolf Canis anthus was studied in the region of Tizi-Ouzou (North-Central Algeria), during a period from November 2016 to May 2017 by means of scat analysis. A total of 386 samples were collected in the study area, 206 in Yakouren and 180 in Ait-Zellal. Prey items found in the dung were assigned to species or higher taxa using identification guides and our collection of plant samples from the study area.The identification of the different items allowed us to classify the prey of this carnivore into 8 food categories Mammals, Arthropods, Birds, Energy plants, Non-energy plants, Reptiles, Molluscs and Waste. Plant material, mammals and birds are the most consumed food items in both study stations, with significant differences between the diet compositions of the two habitats. These results confirm that the feeding behaviour of golden wolves is generalist and opportunistic, the existence of a significant difference between the diet compositions in the two different habitats emphasises the influence of environmental factors on the feeding ecology of Canis anthus.

Parasitic control of wolf behaviour.

Parasitic control of wolf behaviour.

Multi-swarm improved Grey Wolf Optimizer with double adaptive weights and dimension learning for global optimization problems

Grey Wolf Optimizer (GWO) is a swarm intelligent optimization algorithm that simulates the leadership and social behavior of grey wolves to prey. GWO is extensive used in various fields since it has the advantage of being simple and easy to implement. However, when solving complex optimization problems, GWO has insufficient population diversity and unbalanced exploration and exploitation. To overcome these shortcomings of GWO, a multi-swarm improved grey wolf optimizer (MIGWO) is proposed. In MIGWO, firstly, chaotic grouping mechanism is utilized to improve population diversity and dynamic regrouping mechanism to further improve population diversity and balance exploration and exploitation. Secondly, double adaptive weights and dimension learning are utilized to ameliorate the hunting behavior of grey wolves, which can improve search performance. The MIGWO is verified on 53 test problems. The test results show that it is better to other metaheuristic algorithms and GWO variants in solving the optimal solution and convergence accuracy. In addition, MIGWO is utilized to solve 4 engineering design problems.

The socioecology of fear: A critical geographical consideration of human‐wolf‐livestock conflict

Animal fear can be an important driver of ecological community structure: predators affect prey not only through predation, but also by inducing changes in behaviour and distribution—a phenomenon evocatively called the “ecology of fear.” The return of wolves to the western United States is a notable instance of such dynamics, yet plays out in a complex socioecological system where efforts to mitigate impacts on livestock rely on manipulating wolves' fear of people. Examining Washington state's efforts to affect wolf behaviour to reduce livestock predation, we argue that this approach to coexistence with wolves is predicated on relations of fear: people, livestock, and wolves can arguably share landscapes with minimal conflict, as long as wolves are adequately afraid. We introduce the “socioecology of fear” as an interdisciplinary framework for examining the interwoven social and ecological processes of human‐wildlife conflict management. Beyond frequently voiced ideas about wolves' “innate” fear, we examine how fear is (re)produced through human‐wolf interactions and deeply shaped by human social processes. We contribute to the critical physical geography project by integrating critical social analysis with ecological theory, conducted through collaborative interdisciplinary dialogue. Such integrative practice is essential for understanding the complex challenges of managing wildlife in the Anthropocene.

An Improved Grey Wolf Optimization with Multi-Strategy Ensemble for Robot Path Planning.

Grey wolf optimization (GWO) is a meta-heuristic algorithm inspired by the hierarchy and hunting behavior of grey wolves. GWO has the superiorities of simpler concept and fewer adjustment parameters, and has been widely used in different fields. However, there are some disadvantages in avoiding prematurity and falling into local optimum. This paper presents an improved grey wolf optimization (IGWO) to ameliorate these drawbacks. Firstly, a modified position update mechanism for pursuing high quality solutions is developed. By designing an ameliorative position update formula, a proper balance between the exploration and exploitation is achieved. Moreover, the leadership hierarchy is strengthened by proposing adaptive weights of α, β and δ. Then, a dynamic local optimum escape strategy is proposed to reinforce the ability of the algorithm to escape from the local stagnations. Finally, some individuals are repositioned with the aid of the positions of the leaders. These individuals are pulled to new positions near the leaders, helping to accelerate the convergence of the algorithm. To verify the effectiveness of IGWO, a series of contrast experiments are conducted. On the one hand, IGWO is compared with some state-of-the-art GWO variants and several promising meta-heuristic algorithms on 20 benchmark functions. Experimental results indicate that IGWO performs better than other competitors. On the other hand, the applicability of IGWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths. Therefore, IGWO is successfully applied to the path planning as a new method.

Human-directed attachment behavior in wolves suggests standing ancestral variation for human-dog attachment bonds.

Domesticated animals are generally assumed to display increased sociability toward humans compared to their wild ancestors. Dogs (Canis familiaris) have a remarkable ability to form social relationships with humans, including lasting attachment, a bond based on emotional dependency. Since it has been specifically suggested that the ability to form attachment with humans evolved post‐domestication in dogs, attempts to quantify attachment behavior in wolves (Canis lupus) have subsequently been performed. However, while these rare wolf studies do highlight the potential for wolves to express human‐directed attachment, the varied methods used and the contrasting results emphasize the need for further, standardized testing of wolves. Here, we used the standardized Strange Situation Test to investigate attachment behavior expressed in wolves and dogs hand‐raised and socialized under standardized and identical conditions up until the age of testing. We found that 23‐week‐old wolves and dogs equally discriminated between a stranger and a familiar person, and expressed similar attachment behaviors toward a familiar person. Additionally, wolves, but not dogs, expressed significantly elevated stress‐related behavior during the test, but this stress response was buffered by the presence of a familiar person. Together, our results suggest that wolves can show attachment behaviors toward humans comparable to those of dogs. Importantly, our findings demonstrate that the ability to form attachment with humans exists in relatives of the wild ancestor of dogs, thereby refuting claims that this phenotype evolved after dog domestication was initiated.

Optimised Sliding Mode Control of a Hexacopter: Simulation and Experiments

Hexacopters are a kind of unmanned aerial vehicle (UAV) with six actuators and six degrees-of-freedom motions. The control of a hexacopter drone is a critical challenge. This paper presents a nonlinear dynamical model for a hexacopter and complete control approaches based on sliding mode control theory. Furthermore, this study proposed an effective control tuning method based on an optimisation algorithm. The controller has been improved by the grey wolf optimisation (GWO) algorithm, an iteration algorithm inspired by the social hierarchy and hunting behaviour of grey wolves. The improvement of the controller has been verified both experimentally and in simulations. The performance of the sub-controller for an attitude angle was tested in a test bench, and the whole flight controller was tested in simulation hexacopters, which are highly manoeuvrable, nonlinear aerial vehicles with six independent rotors and capacity for vertical take-off and landing. This article presents a derivation of the nonlinear dynamical model for a hexacopter, which includes the aerodynamic drag, and inertia counter torques. Flight control based on sliding mode control theory, which generally shows good performance on nonlinear systems, is developed and implemented. Given the need to simultaneously tune controller parameters, two nature inspired optimisation routines (GWO and PSO) are applied and compared for effectiveness in controller tuning. Results indicate that GWO-based tuning produces superior outcomes in terms of controller performance. This is in addition to the fact that PSO parameters require tuning rather than random selection of algorithm parameters. A reduced-order physical prototype is presented for the validation of the tuning routine on the roll/pitch control. The results indicate good agreement between simulation and experimental outcomes, with about 10.4% improvement in the tracking performance of roll DOF when GWO is applied to tune the controller.