Dingo optimization algorithm

Recycled Aggregate Concrete (RAC) plays an essential role in sustainable construction by reusing materials from demolished structures and reducing environmental pollution. However, accurately predicting its elastic modulus remains a major challenge due to the variability in material composition and the limitations of traditional empirical formulas. This study aims to improve the prediction accuracy of RAC’s elastic modulus using hybrid machine learning and optimization techniques. Two advanced machine learning models, Adaptive Boosting Regression (ADAR) and Stacking Regression (Stacking), were implemented for their strong learning capabilities. To enhance the predictive performance of these models, the Electrostatic Discharge Algorithm (EDA) was used as an optimizer, creating two hybrid frameworks: ADED (ADAR + EDA) and STED (Stacking + EDA). These models were trained and validated using a comprehensive dataset, and their performance was evaluated through statistical metrics, including R², RMSE, MSE, WAPE, and N10_Index. The results showed that the ADED model achieved the highest accuracy, outperforming other models, while the STED model ranked second with R² = 0.982, RMSE = 0.953, MSE = 0.909, WAPE = 0.040, and N10_Index = 0.955. The findings confirm that hybrid models combining machine learning and optimization significantly improve the prediction of the elastic modulus of RAC, providing a reliable approach for sustainable design and structural analysis in modern construction.

This article presents a Modified Method for tuning the parameters of a power system stabilizer (PSS). This article suggests a different approach that modifies the Black Kite Algorithm (BKA). The Black Kite (BKA) method is inspired by the migratory and predatory habits of the black kite. BKA combines the Leader and Cauchy mutation strategies to improve the algorithm's capacity for global search and convergence rate. This article includes comparative simulations of the PSS objective function and transient response to verify the effectiveness of the suggested strategy. The study validates the proposed method through comparison with both conventional techniques and the original BKA. Simulation results demonstrate that, when benchmarked against competing algorithms, the proposed method consistently yields optimal performance and exhibits faster convergence in certain scenarios. Notably, it reduces undershoot and overshoot by an average of 65% and 90.22%, respectively, compared to the PSS-Lead Lag method. Furthermore, the proposed approach not only minimizes overshoot and undershoot but also achieves a significantly faster settling time.

The Internet of Things (IoT) facilitates the seamless integration of diverse physical devices with the Internet, enabling groundbreaking applications across sectors such as defense, transportation, agriculture, and healthcare. These applications have gained significant traction due to their capacity to address real-time challenges efficiently. Nevertheless, IoT systems are inherently vulnerable to security threats, exposing them to various cyberattacks that can compromise their functionality and reliability. To address these challenges, a novel Dingo Optimized hierarchicaL Auto-Associative pOlynomial convolutional neural networks for Intrusion Detection (DOLO-ID) approach has been proposed to enhance security and detect intrusion effectively. The raw data is pre-processed through cleaning and normalization to enhance quality and usability. Feature selection is achieved using the Dingo Optimization which iteratively identifies and optimizes the most relevant features for classification tasks. The selected features are fed into a deep learning architecture incorporating a HAPP CNN Network for accurate classification of intrusions into categories such as attack or normal. The f1score, recall, precision, and accuracy of the proposed DOLO-ID method are 92.8%, 91%, 92% and 98.56% which is higher than the existing techniques.

Home automation systems are evolving rapidly with advancements in machine learning (ML) and gesture-based controls, enhancing user convenience and safety in smart environments. Traditional automation interfaces lack intuitive control and adaptability, making integrating ML-driven gesture recognition and safety monitoring essential. Eventbased sensors capture dynamic, high-resolution data, allowing asynchronous gesture interpretation and optimized device control. ML models, including convolutional and recurrent neural networks, improve gesture recognition, while safety monitoring systems identify hazards like falls and dangerous proximity for vulnerable residents. This paper comprehensively reviews current advancements in gesture control, safety monitoring, and energy efficiency within home automation. It highlights the efficacy of technologies such as event-based cameras, sensor networks, and ML algorithms while addressing limitations in accuracy under varying environmental conditions. A comparative analysis suggests future improvements in adaptive, secure, and energy-efficient systems that support personalized, user-centered automation.

This research study delves into the domain of civil engineering, specifically focusing on the prediction of compressive strength (f'c) in Recycled Aggregate Concrete (RAC). As the construction industry seeks sustainable solutions, RAC has gained prominence due to its environmentally friendly nature. However, accurately predicting the f'c of RAC remains a complex challenge, owing to the inherent variability of recycled materials. To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). The performance of the LSSVR model is enhanced through the utilization of these optimizers, resulting in its increased proficiency in predicting the f'c of RAC. Through comprehensive experimentation and rigorous analysis, this research showcases the effectiveness of the proposed hybrid method. The findings illustrate significant improvements in the accuracy and reliability of f'c predictions for RAC compared to traditional methods. Moreover, this investigation provides significant perspectives on the synergy between ML and optimization techniques in civil engineering. The most reliable outcomes in this study were achieved through the hybridization of the LSSVR model with GTO. The resulting LSGT model attained the highest R2 value of 0.989 and the lowest RMSE value of 1.618. Overall, the integration of LSSVR with GTO and DOA emerges as a promising methodology to enhance f'c prediction in RAC. This research enhances the current understanding of RAC and highlights its potential for robust hybrid machine-learning approaches in solving real-world challenges within civil engineering.

The dingo optimization algorithm (DOA) adopts the social life of dingo dogs. The dingo is a breed of ancient dog originating from Australia. Dingo hunting strategies such as assault with persecution, flocking, and scavenging behavior became the inspiration for DOA. In this paper, DOA is applied to a power system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a single-machine infinite bus (SMIB). DOA is used to obtain optimal parameters for PSS. The damping controller is designed for optimal lead-lag control. To obtain the performance of the DOA method, the results were compared with the uncontrolled method, conventional PSS, Whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Simulation using MATLAB with three different operating conditions, namely light load (20%), medium load (50%) and high load (100%). From the simulation using MATLAB with SMIB modeling, it was found that the application of the DOA method on PSS has the ability to red...

The issue of climate modification and human actions terminates in a chain of hazardous developments, comprehensive of landslides. The traditional approaches of observing the environmental attributes that is actually obtaining rainfall data from places can be cruel and suppressing supervising necessitated for careful infliction. Thus, landslide forecasting and early notice is a significant application via wireless sensor networks (WSN) to reduce loss of life and property. Because of the heavy preparation of sensors in landslide prostrate regions, clustering is a resourceful method to minimize unnecessary transmission. In this article we introduce Q-learning based forecasting early landslide detection (Q-LFD) in internet of things (IoT) WSN. The Q-LFD mechanism utilizes a dingo optimization algorithm (DOA) to choose the best cluster head (CH). Furthermore, the Q-learning algorithm forecast the landslide by soil water capacity, soil layer, soil temperature, Seismic vibrations, and rainfall. Experimental results illustrate the Q-LFD mechanism raises the landslide detection accuracy. In addition, it minimizes the false positive, false negative ratio.

Gastric cancer (GC) is one of the leading causes of mortality from cancer around the world. It primarily affects older persons. Every year, almost six out of ten persons diagnosed with stomach cancer are above the age of 65. This paper proposed a novel GAstric cancer Detection using DEep LEarning (GADDLE) to identify the gastric cancer in an CT image. Initially the input images are pre-processed using the Gaussian adaptive bilateral filter to enhance the quality of the image. Therefore, the pre-processed images are fed into RegNet feature extraction model to for extracting the features in the image. The best features are selected by using the Dingo Optimization Algorithm. Finally, the normal and the abnormal case of the GC are classified using Link Net model. The GasHisSDB datasets are used to evaluate the performance of the proposed method of specific matrices such as Specificity, Recall, Accuracy, Precision and F1-Score. The suggested Link Net improves accuracy by 2.43%, 4.89%, and 1.98% compared to Alex Net, Google Net, and ResNet, respectively.

A novel approach for efficient automatic voltage regulation (AVR) control using hybridized artificial neural network (ANN) model has been proposed in this research work. The novel automatic voltage regulator tuning using an improved neural network has been proposed in this paper. Artificial neural network has been used as focused time delay neural network (FTDNN). Validation is performed by comparing with the methods of feed-forward backpropagation neural networks, cascade-forward backpropagation neural networks, Elman-recurrent neural networks, focused time-delay neural networks and Distributed Time Delay Neural Networks (DTDNN). This hybridized ANN model incorporated a metaheuristic method namely Slime Mold Algorithm (SMA) for obtaining improved result on AVR control. SMA has characteristics that uses adaptive weights to simulate the process to generate feedback from the movement of biooscillator-based slime molds in foraging, exploring, and exploiting areas. The performance of the proposed method is focused on speed and rotor angle. The proposed method is compared with other neural network methods in a broad set of benchmarks to verify system efficiency. Promising results were obtained in tuning the AVR under 30 %, 60 % and 90 % loading conditions at slime mold count of 50.

In the power system, any change in the input or any disturbance causes oscillations in frequency, voltage, and real and reactive power. The Power System Stabilizer (PSS) stands out as a renowned and effective equipment for damping power system oscillations, extensively explored in studies aimed at enhancing the dynamic stability of power systems. Challenges such as low-frequency fluctuations and the intricate nature of power systems have spurred the application of intelligent methods and optimization algorithms for addressing PSS-related issues. In this paper, a brief review of the studies conducted in the field of meta-heuristic optimization algorithms for PSS design is presented. Meta-heuristic methods are divided into three groups, and based on them, more than 200 articles have been studied in the field of PSS parameter optimization. The balance between exploration and exploitation is a common theme among all meta-heuristic methods. Researchers and practitioners of meta-heuristic optimization methods belong to a wide range of audiences in the fields of power systems optimization, who will benefit from this research.

A power system stabilizer (PSS) is a device that provides additional damping torque to the power system during the mitigation of oscillations caused by various sorts of disturbances. Bio-Inspired Optimization Algorithms are used to construct PSS on a Single Machine Infinite Bus (SMIB) system and two areas, respectively, in this research. The study examines four different multi-machine generator systems working under a variety of disturbances. The SMIB system uses ISE as an objective function, while the Multimachine system uses Eigen value shifting. Modified Heffron-Phillips (MHP) model is used in the SMIB system to lower the system complexity and processing time. The SMIB system's response time is improved by integrating a PID controller with MPSS. Particle swarm optimization (PSO), whale optimization (WO) and butterfly optimization (BO) techniques are used to find the best parameters for PSS and PID. A thorough evaluation of the MPSS, MPSS-PID, and other optimization algorithms' performance is offered based on the simulation findings.

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A...

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A...

One of the most affordable methods for improving the performance of radial distribution networks is through the deployment of capacitors. However, the optimal allocation of capacitors is a serious issue that must be resolved. This paper proposed a novel dingo optimization algorithm (DOA) to tackle the problem of the allocation of capacitors. A single objective function, subjected to several equality and inequality constraints, was formulated and solved using the proposed DOA for capacitor allocation. The performance of the network was assessed using voltage profile, real power, and reactive power losses as the performance metrics. The results indicated a significant improvement in the performance of the network as real power as well as reactive power losses were greatly reduced by 34.44 and 35.20%, respectively, and the overall network voltage profile was enhanced. Thus, DOA proved effective in the allocation of capacitors in radial distribution networks.

One of the most affordable methods for improving the performance of radial distribution networks is through the deployment of capacitors. However, the optimal allocation of capacitors is a serious issue that must be resolved. This paper proposed a novel dingo optimization algorithm (DOA) to tackle the problem of the allocation of capacitors. A single objective function, subjected to several equality and inequality constraints, was formulated and solved using the proposed DOA for capacitor allocation. The performance of the network was assessed using voltage profile, real power, and reactive power losses as the performance metrics. The results indicated a significant improvement in the performance of the network as real power as well as reactive power losses were greatly reduced by 34.44 and 35.20%, respectively, and the overall network voltage profile was enhanced. Thus, DOA proved effective in the allocation of capacitors in radial distribution networks.

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA's results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A, B, and C increased real power losses by 825.8, 136.2, and 13.2%, respectively, and reactive power losses by 72.86, 52.30, and 31.89%. Distribution networks should not be evaluated on a single-phase basis since losses differ in each of the three phases. Conductor size and spacing reductions cause huge losses.

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A, B, and C increased real power losses by 825.8, 136.2, and 13.2%, respectively, and reactive power losses by 72.86, 52.30, and 31.89%. Distribution networks should not be evaluated on a single-phase basis since losses differ in each of the three phases. Conductor size and spacing reductions cause huge losses.

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA's results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA's results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA's results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.

The dingo optimization algorithm (DOA) adopts the social life of dingo dogs. The dingo is a breed of ancient dog originating from Australia. Dingo hunting strategies such as assault with persecution, flocking, and scavenging behavior became the inspiration for DOA. In this paper, DOA is applied to a power system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a single-machine infinite bus (SMIB). DOA is used to obtain optimal parameters for PSS. The damping controller is designed for optimal lead-lag control. To obtain the performance of the DOA method, the results were compared with the uncontrolled method, conventional PSS, Whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Simulation using MATLAB with three different operating conditions, namely light load (20%), medium load (50%) and high load (100%). From the simulation using MATLAB with SMIB modeling, it was found that the application of the DOA method on PSS has the ability to red...

The dingo optimization algorithm (DOA) adopts the social life of dingo dogs.
The dingo is a breed of ancient dog originating from Australia. Dingo hunting
strategies such as assault with persecution, flocking, and scavenging behavior
became the inspiration for DOA. In this paper, DOA is applied to a power
system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a
single-machine infinite bus (SMIB). DOA is used to obtain optimal
parameters for PSS. The damping controller is designed for optimal lead-lag
control. To obtain the performance of the DOA method, the results were
compared with the uncontrolled method, conventional PSS, whale
optimization algorithm (WOA), and grasshopper optimization algorithm
(GOA). Simulation using MATLAB with three different operating conditions,
namely light load (20%), medium load (50%) and high load (100%). From the
simulation using MATLAB with SMIB modeling, it was found that the
application of the DOA method on PSS has the ability to reduce the average
undershoot value by 28.16% and reduce the average undershoot value to
65.57% compared to the conventional PSS method.