•Gray wolf optimizer (GWO) is employed in solving the optimal reactive power dispatch (ORPD) problems.•Three case studies have been utilized to show the effectiveness of GWO.•GWO able to find minimum ...loss and voltage deviation solution than those determined by other techniques.
This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves’ leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers’ ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by ...reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.
Recycled aggregates (RAGs) usage in concrete is surging, inspired by environmental and economic concerns. Regarding predicting various models designed the values of modulus of elasticity (MOE) of ...concrete with natural aggregates and, in conclusion, they would probably be unreliable when used to concrete with RAG. In the present study, two new gray wolf multi‐layer perceptron neural networks (GWMLP) and gray wolf support vector regression (GWSVR) algorithms were proposed to predict RAG concrete's elastic modulus. About 400 records were gathered from published articles to develop these models. The results show that among the GWMLP models with different hidden layers, GWM3L with three hidden layers could get the highest score (TRS) at 39. Simultaneously, in the testing phase, the GWSVR was the first‐rank model because of the lower RMSE (0.6381), MAE (0.1541), and a larger R2 (0.9707) compared with GWMLP models. Therefore, it can result that the GWSVR model could predict the elastic modulus of RAG concrete precisely even better than GWM3L, which is well over the accuracy of the developed models.
Several areas of Iran are prone to numerous natural hazards. An effective multi-hazard risk reduction requires analysis of the individual hazards and their interplay. This research develops a ...multi-hazard probability map for three hazards (i.e. landslides, floods, and earthquakes) for the management of hazard-prone areas in Lorestan Province, Iran, using anew ensemble model named SWARA-ANFIS-GWO. First, based on flood and landslide occurrence maps, hazard-prone areas were identified and sub-divided into two subsets.70% of these locations were randomly chosen to be used for the construction of susceptibility maps, while the remaining 30% of the instances were used to assess the accuracy of the models. Then, eleven factors relating to terrain and land use were selected for the preparation of landslide and flood susceptibility maps. An earthquake map was prepared based on a probabilistic seismic hazard analysis (PSHA). The SWARA method was implemented for weighting contributing factors and evaluating spatial relationships between the three hazards and predisposing factors. Subsequently, the ANFIS approach was used to acquire weights for each value while using a gray Wolf metaheuristic algorithm. Finally, all weight values were further assessed using the MATLAB software. The predicated results from the models were validated with ROC (rate of change) curves. The resulting AUCs (area under the curve) of the validation data indicated accuracies of 84% and 80% for floods and landslides, respectively, and 87% and 82.6%for flood and landslides based on the training data, respectively. Finally, the flood, landslide, and earthquake maps were combined to create a multi-hazard probability map of the Lorestan Province. This multi-hazard map serves as a valuable tool for land use planning and sustainable infrastructure development for the Lorestan Province.
Display omitted
•Multi-hazard probability assessment and mapping for the management of hazard-prone areas in Lorestan Province, Iran•Using SWARA-ANFIS-GWO new ensemble model for multi-hazard probability mapping•Considering three hazards including landslides, floods, and earthquake in the study area
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key parameter for the successful accomplishment of a tunneling project, and the proper and reliable prediction of ...this parameter can lead to minimizing the risks associated to high capital costs and scheduling for such projects. This research aims at optimizing the hyper-parameters of the support vector machine (SVM) technique through the use of three optimization algorithms, namely, gray wolf optimization (GWO), whale optimization algorithm (WOA) and moth flame optimization (MFO), in forecasting TBM AR. In fact, the role of these optimization techniques is to optimize the hyperparameters ‘C’ and ‘gamma’ of the SVM model to get higher performance prediction. To develop the hybrid SVM-based models, 1,286 sample sets of data collected from a water transfer tunnel in Malaysia comprising seven input variables, i.e., rock mass rating, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force and revolution per minute, and one output variable, i.e., TBM AR, were considered and used. Several GWO-SVM, WOA-SVM and MFO-SVM models were constructed to predict TBM AR considering their effective parameters. The accuracy levels of the proposed models were assessed using four statistical indices, i.e., the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models. R2 of (0.9623 and 0.9724), RMSE of (0.1269 and 0.1155), and VAF of (96.24 and 97.34%), respectively, for training and test stages of the MFO-SVM model confirmed that this hybrid SVM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
Accurate state of health (SOH) estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. However, traditional neural network algorithms to estimate ...SOH often focus on fitting nonlinear fluctuation and is weak in the overall tracking trend. This paper thus proposes an improved radial basis function neural network (IRBFNN) to estimate the SOH with the simultaneous fitting of general trends and local fluctuations. A polynomial is provided to describe the overall trend of SOH. Meanwhile, the hidden layer of the IRBFNN converts the features nonlinearly to simulate the local battery capacity regeneration. Moreover, the initial parameters of the IRBFNN are obtained after training and then optimized by the improved gray wolf optimization algorithm. Two different datasets are utilized to verify the effectiveness of the presented method by comparing it with several other algorithms. Experimental results show that the IRBFNN-based method can accurately estimate the SOH, and the maximum estimation errors are within ±4%. Therefore, the results imply that the proposed method can effectively alleviate the problem of the poor estimation performance of traditional neural network-based algorithms in the later stage of battery aging.
•A linear polynomial is proposed to show the global linear change of SOH.•The local nonlinear change of SOH is shown by the RBF neural network.•The improved gray wolf optimization is used to optimizing the network parameters.•The four features are extracted in the partial constant current charging process.•Experimental results highlight that the proposed method is more accurate.
Genetic rescue – ameliorating inbreeding depression and restoring genetic diversity of inbred populations through gene flow - is valuable in wildlife conservation. Empirically validated ...recommendations for genetic rescue supported by evolutionary genetics theory advise maximizing genetic diversity in target populations. Instead, recent papers based on genomic studies of island foxes, Isle Royal wolves, and simulation modeling claim it would be preferable to minimize introduction of harmful variation by avoiding genetic rescue altogether or by selecting partially-inbred sources presumed to have fewer harmful alleles. We examined the assertions and evidence underlying these new recommendations. The claim that long-term persistence of a few small inbred populations invalidates the small population paradigm commits the survivorship fallacy by ignoring population extinctions through inbreeding. The claim that island foxes show no inbreeding depression conflicts with elevated levels of putatively harmful alleles, low fecundity, and island-specific disease susceptibilities. The claim that the history of Isle Royale wolves represents likely outcomes of genetic rescues using immigrants from larger source populations is invalid: the unplanned addition of a single male to an inbred population capped at ~25 individuals does not represent sound genetic rescue. The simulations in Robinson et al. (2018, 2019) and Kyriazis et al. (2019 pre-print) apply several unrealistic assumptions and parameter distributions that disfavor large, outbred sources for genetic rescue. Accordingly, the simulations' conclusions conflict profoundly with those of >120 meta-analysed real datasets, and do not overturn current empirically validated recommendations to maximize genetic diversity in the target population.
•Inbreeding and loss of gene diversity are unavoidable in small isolated populations and increase extinction risks•Such populations can be often be rescued by gene flow from another population (genetic rescue)•Gene flow from genetically diverse populations is better at reversing genetic erosion than that from small populations•Proposals to minimize introduction of harmful variation are based on unrealistic simulations that contradict real-life outcomes•Maximizing genetic diversity in the target population is the best current strategy to improve fitness and ability to evolve
Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is ...different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function’s optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm.
•Grey wolf optimization algorithm is the object of study.•Defect of GWO is pointed out and the reason is determined.•Other optimization algorithms may have a similar defect.•The test method is proposed, and it works well.•Looking forward to the further development of optimization algorithms.
Exploring the diet of arctic wolves Dalerum, F; Freire, S; Angerbjorn, A ...
Canadian journal of zoology,
03/2018, Volume:
96, Issue:
3
Journal Article
Peer reviewed
The grey wolf (Canis lupus Linnaeus, 1758) is one of the most widespread large carnivores on Earth, and occurs throughout the Arctic. Although wolf diet is well studied, we have scant information ...from high Arctic areas. Global warming is expected to increase the importance of predation for ecosystem regulation in Arctic environments. To improve our ability to manage Arctic ecosystems under environmental change, we therefore need knowledge about Arctic predator diets. Prey remains in 54 wolf scats collected at three sites in the high Arctic region surrounding the Hall Basin (Judge Daly Promontory, Ellesmere Island, Canada, and Washington Land and Hall Land, both in northwestern Greenland) pointed to a dietary importance of arctic hare (Lepus arcticus Ross, 1819; 55% frequency of occurrence) and muskoxen (Ovibos moschatus (Zimmermann, 1780); 39% frequency of occurrence), although we observed diet variation among the sites. A literature compilation suggested that arctic wolves (Canis lupus arctos Pocock, 1935) preferentially feed on caribou (Rangifer tarandus (Linnaeus, 1758)) and muskoxen, but can sustain themselves on arctic hares and Greenland collared lemmings (Dicrostonyx groenlandicus (Traill, 1823)) in areas with limited or no ungulate populations. We suggest that climate change may alter the dynamics among wolves, arctic hare, muskoxen, and caribou, and we encourage further studies evaluating how climate change influences predator--prey interactions in high Arctic environments.
By dint of the advantage of deeply integrating empirical knowledge and monitoring data, the belief rule base (BRB) is widely used to assess the performance of complex systems, including multiagent ...systems. However, the existing paradigms for designing the inputs of BRB systems necessitate sampling discretization when faced with continuous signal inputs, which poses the risk of information loss and reduces the effectiveness of performance assessment. As such, the BRB with continuous inputs (BRB-CI) is constructed based on the continuous wavelet transform, a typical joint time-frequency analysis technique, enabling BRB systems to handle continuous signal inputs directly. The stability of the BRB-CI is proven through output error analysis. A new structure optimization strategy aimed at simplifying the BRB-CI by removing redundant belief rules is developed. Moreover, a new parameter optimization approach based on the improved gray wolf optimizer with interpretability reinforcement is devised, contributing to the interdisciplinary research on BRB systems and metaheuristic algorithms. Several experiments are conducted, demonstrating the novelty, superiority, and engineering practicability of the proposal.
PDF
