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Saving the endangered gray wolf
Readers will learn about the gray wolf and its behaviors, as well as the efforts to bring the wolf back. Highly informative and likely to appeal to animal lovers.
Book
Power efficiency improvement in reactive power dispatch under load uncertainty
Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios using the normal probability density function, while a reduction-based technique is implemented to decrease the number of those scenarios. The improved gray wolf optimization (I-GWO) algorithm is introduced for the first time to address the stochastic ORPD problem. Experimentation is conducted on an IEEE-30 bus system when results are contrasted with conventional gray wolf optimization (GWO) and five other algorithms as stated in the literature. The I-GWO algorithm's performance is assessed with and without considering load demand uncertainty. Through Friedman's statistical tests, a significant decrease of 20.96% in active power losses and 63.06% in the summation of expected power losses is observed. The I-GWO algorithm's results on the ORPD problem demonstrate its effectiveness and robustness.
Journal Article
Gray wolves : howling pack mammals
\"Gray wolves are known as howling pack mammals. But how are they similar to and different from other mammals, ranging from coyotes to manatees? Readers will compare and contrast key traits of gray wolves to traits of other mammals.\"-- Provided by publisher.
Book
Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles
The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.
Journal Article
Application of Multistrategy Improvement Gray Wolf Algorithm to Optimize Extreme Gradient Boosting in Emergency Triage
Effective triage in the emergency department (ED) is essential for optimizing resource allocation, improving efficiency, and enhancing patient outcomes. Conventional systems rely heavily on clinical judgment and standardized guidelines, which may be insufficient under growing patient volumes and increasingly complex presentations.
We developed a machine learning triage model, MIGWO-XGBOOST, which incorporates a Multi-strategy Improved Gray Wolf Optimization (MIGWO) algorithm for parameter tuning. Missing data were processed, and the dataset was randomly split into 80 percent for training and 20 percent for testing. Model performance was evaluated against standard XGBOOST, GWO XGBOOST, AdaBoost, LSTM, and CNN-BiGRU.
MIGWO-XGBOOST improved accuracy by 8.5 percent over unoptimized XGBOOST and reduced optimization time by 9,285 seconds relative to GWO-XGBOOST. Compared with other benchmarks, accuracy gains were 12.5 percent over AdaBoost, 3.3 percent over LSTM, and 1.9 percent over CNN-BiGRU. These results demonstrate both predictive strength and computational efficiency in complex data environments.
MIGWO-XGBOOST provides a robust framework for rapid and precise triage decisions in the ED. By enhancing accuracy while substantially reducing computational time, this approach demonstrates the potential of advanced machine learning to support emergency decision-making and optimize patient care pathways.
Journal Article
Arctic wolf : the high arctic
Introduces readers to the wolverine and shows how they survive in their bone-chilling environment, including how they hunt, stay warm, and raise their babies.
Book
Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System
Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.
Journal Article
Saving the gray wolf
In this book, early fluent readers will learn how the gray wolf came back from the brink of extinction.
Book
Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm
The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.
Journal Article