Whale Optimization Research Articles

Optimization of Laminar Boundary Layers in Flow over a Flat Plate Using Recent Metaheuristic Algorithms

Heat transfer is one of the most fundamental engineering subjects and is found in every moment of life. Heat transfer problems, such as heating and cooling, where the transfer of heat between regions is calculated, are problems that can give exact solutions with parametric equations, many of which were obtained by solving differential equations in the past. Today, the fact that heat transfer problems have a more complex structure has led to the emergence of multivariate models, and problems that are very difficult to solve with differential equations have emerged. Optimization techniques, which are also the subject of computer science, are frequently used to solve complex problems. In this study, laminar thermal boundary layers in flow over a flat plate, a sub-problem of heat transfer, is solved with recent metaheuristic algorithms. Teaching learning-based optimization (TLBO), sine cosine optimization (SCO), gray wolf optimization (GWO), whale optimization (WO), salp swarm optimization (SSO), and Harris hawk optimization (HHO) algorithms are used in the study. In the optimization problem, the laminar boundary layer thickness, heat flow, and distance from the leading edge are determined. These three models’ minimum, maximum, and target values are found under the specified design variables and constraints. In the study, 540 optimization models are run, and it is seen that HHO is the most suitable optimization technique for heat transfer problems. Additionally, SSO and WO algorithms gave results close to HHO. Other algorithms also set model targets with an average of less than 0.07% and acceptable error rates. In addition, the average problem solution time of all optimization algorithms and all models was 0.9 s. To conclude, the recent metaheuristic algorithms are found to be powerful and fast in solving heat transfer problems.

Multi-level Kapur’s thresholding using whale optimization and social group optimization for brain MRI image segmentation

Image thresholding is the vital part for segmentation of a region in image. A fresh technique of multilevel thresholding based on the whale optimizer and social group optimizer are in fact recommended in this particular paper. New Optimization techniques inspired are actually affected as a result of the interpersonal as well as the backpacking behaviour of all of the whales. The process of multi-level thresholding problem is applied through the Kapur’s entropy and metaheuristic algorithms. The suggested strategy is tried on brain MRI test image set. The suggested methods are next in contrast with particle swarm optimization (PSO) and differential evaluation (DE) constructed multilevel thresholding techniques and other optimization techniques in the literature. The segmentation really calculated with evaluation measures like Sensitivity, Specificity, and Dice Score (DS).

Hybrid eagle strategy for QOS-based cloud service composition

Web Services are the collections of open protocols and standards used for exchanging data between applications and systems. Cloud computing is a prominent computer technology, and technical users are receiving most of the benefits from it as it is dispersing information technology based capabilities to the customers in terms of ‘web services’ with flexibility and ease of use. As the use of cloud computing grows on a daily basis, service deployment also improved on a daily basis. Cloud computing has made significant progress and has advanced to the next stage of growth by adding new technologies to fulfil the current demands of consumers where the consumers can make ease of using resources in the form of platform, software or infrastructure on demand without administrating the underlying intricacy of the technology. Nowadays, multiple services provided by cloud platforms with analogous efficacies are available to the users which can be attributed as Quality of Service (QoS) parameters. Selecting an optimal cloud service which can satisfy the consumers’ requirements is often found to be a tedious task and quiet challenging. Diverse metaheuristic has been put forward in resolving the problem. To address this problem, in this paper we proposed a Multi-Criteria Decision Making methodology with a technique Simple Additive Weighting (SAW) which can help customer in determining the most preferable cloud service among the pool of existing service according to their satisfaction. As the same time we propose an enhanced Eagle Strategy with Whale Optimization (ESWOA) methodology to maintain equilibrium among local as well as global optimum. This system can recommend time-saving dependable trustworthy cloud services from a pool of accessible cloud services.

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer

Sliding pressure control is a well-known method of controlling supercritical power plants that improves energy efficiency and reduces pressure dynamic stresses. This paper presents a novel approach for developing a supercritical cleaner coal power plant’s sliding pressure control strategy. First, using Whale Optimizer, a nonlinear identified transfer matrix model was created (WO). By comparing simulations and errors, the WO clearly outperforms the GA and Grey-Wolf Optimizer (GWO) techniques on parameter identification. The model also includes a multivariable PI/PD controller for improved plant operation. Again, WO controller tuning outperformed GA and GWO in terms of pressure deviations, power deviations, rise time, and fuel usage. It is now argued that the WO is superior to other techniques in modeling and controlling system dynamics, energy efficiency, and cleaner operation.

Analysis of Grid-Interactive PV-Fed BLDC Pump Using Optimized MPPT in DC–DC Converters

In solar photovoltaic (PV) system-based Brushless DC (BLDC) motors for water pumping application, the role of DC/DC converters is very important. In order to extract the maximum power from the PV array, an efficient DC/DC converter is essential at the intermediate stage. In this work, different DC/DC converter topologies suitable for BLDC motors are proposed. The converters are supported by an optimized maximum power point tracking system to provide a reliable operation. Recent optimization algorithms such as fuzzy logic, perturb and observe, grey wolf, and whale optimization are implemented with the PI controller in maximum power point tracking to maximize the conversion efficiency. The obtained results using SEPIC, LUO, and interleaved LUO converters provide a comparative study in the case of converter output, motor parameters, and grid output. The performance analysis on three different converters and multiple optimization methods are carried out. By analyzing the performance of different converter topologies, the interleaved LUO converter outperforms the other two converters with the results of a voltage gain ratio of 1:22, conversion efficiency of 98.3%, and grid current THD of 2.9%. Moreover, regarding the power quality aspect, the total harmonic distortion of the grid current is maintained below the IEEE-519 standard. In addition, the developed system has an advantage of operating both in stand-alone and grid-connected operation modes.

Dolphin echolocation optimized sailfish algorithm for energy efficient and secured routing based on trust management in ZigBee wireless sensor network

AbstractSecurity is the vital phenomenon of wireless sensor network (WSN) to preserve the energy. Traditional techniques does not concern the security of routing purpose, it has limitation like low energy resource, unsecure communication channel. In this article, a dolphin echolocation optimized sailfish (DESF) algorithm for energy efficient (EE) and secured routing based on trust management in ZigBee WSN is proposed. The proposed method is combination of enhanced shortcut tree routing based dolphin echolocation sailfish (EOSTR) and DESF, hence it is called EOSTR‐DESF. Here, hybrid DESF with enhanced routing technique is used to select the optimal route. The proposed approach is implemented in NS2 and the performance of the proposed collaborative trust and tree routing based on DESF in ZigBee is compared with the existing approaches like particle swarm optimization‐ant colony optimization, firefly algorithm‐Hirschberg Sinclair, and whale optimization algorithm‐simulated annealing algorithm. Moreover, the performance is analyzed with metrics like delay, packet delivery ratio, energy consumption, throughput, and residual energy. The simulation results demonstrate that the proposed method is more efficient with varying nodes of 33.25%, 20.17%, 8.04% for different data transfer rate of 31.36%, 15.62%, 9.10%.

Hybrid slime mould - Grey wolf optimization algorithm for efficient feature selection

Selection of features is an effective method for minimizing the amount of data features in order to improve machine learning classification performance. Choosing a set of attributes is a high-level procedure for selecting a collection of relevant features. To boost the classifier's performance, use a dimensional dataset. We outline a typical feature selection problem in this work in order to minimize the amount of role and responsibilities while improving accuracy. Different classification dataset from the Machine learning repository have been used to test SMA with GWO as a feature selection strategy. The feature selections for UCI repository datasets include Bat optimization, Cuckoo search optimization, Slime mould optimization, Whale optimization, Particle swarm optimization, and Grey wolf optimization.

Cooperative multi-function approach: A new strategy for autonomous ground robotics

The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is performed utilizing a single optimization technique with a particular objective function. This although simplifies the process but greatly compromise the map accuracy and exploration depth. Realizing this aspect, we present a new exploration technique with augmented objective functions, so as maximize the search area and map accuracy of the exploration space. The proposed framework termed as Multi-Objective Whale Optimization Algorithm (MO-WOA), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale’s population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The algorithm utilizing this optimization approach formulates the optimal way-points. The performance metrics are presented through extensive simulations. The results efficacy is then demonstrated by comparing the results of the proposed algorithm with those achieved from contemporary techniques namely Coordinated Multi-Robot Exploration (CME), conventional Whale Optimizer (WO) integrated with CME and Arithmetic Optimization (AO). Results demonstrate that the proposed algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.

Facial expression recognition system based on variational mode decomposition and whale optimized KELM

In this paper, a methodology for the Facial Expression Recognition (FER) system is proposed using the Variational Mode Decomposition (VMD) and Whale Optimization (WO) with Kernel Extreme Learning Machine (KELM) classifier. A non-stationary, adaptive, and variational signal analysis technique called VMD is adopted in this work, which depends on the signal's frequency information content. The VMD decomposed the input image into four modes, and the 4th mode of the VMD decomposition is considered for feature representation, a high-frequency band. This VMD mode preserves the edge and shape features from the face image efficiently. The high dimensional features are reduced using the Principal Component Analysis + Linear Discriminant Analysis (PCA + LDA) method, which minimizes the feature dimension and retains the high variance among emotion classes. A hybrid classifier with better scalability and faster learning speed than SVM and Least Squares SVM (LS-SVM), namely WO-KELM, is proposed to discriminate facial expressions accurately. The WO algorithm is employed for optimal parameter tuning of the KELM with the RBF kernel. The performance of the proposed framework are compared with state-of-the-art methods. Extensive experiments are assessed on the two benchmark datasets, namely Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK+). Experimental results founded on 5-fold Stratified Cross-Validation (SCV) test reveal the superiority of the proposed method over state-of-the-art systems.

Parameter Extraction of Solar Module Using the Sooty Tern Optimization Algorithm

Photovoltaic module parameter estimation is a critical step in observing, analyzing, and optimizing the efficiency of solar power systems. To find the best value for unknown parameters, an efficient optimization strategy is required. This paper presents the implementation of the sooty tern optimization (STO) algorithm for parameter assessment of a solar cell/module. The simulation findings were compared to four pre-existing optimization algorithms: sine cosine (SCA) algorithm, gravitational search algorithm (GSA), hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), and whale optimization (WOA). The convergence rate and root mean square error evaluations show that the STO method surpasses the other studied optimization techniques. Additionally, the statistical results show that the STO method is superior in average resilience and accuracy. The superior performance and reliability of the STO method are further validated by the Friedman ranking test.

Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms

The construction of metropolises in smart cities is the trend of developed countries. However, it may cause damages to the surrounding structures. For this reason, the diaphragm wall has been applied to prevent the deformation or collapse of the surrounding structures. Diaphragm walls can be deflected due to the swelling pressure or other geotechnical properties during construction. Therefore, the accurate prediction of diaphragm wall deflection (DWD) is challenging in construction aiming to ensure the safety of the surrounding structures. This study is, therefore, to propose two intelligent models for predicting DWD induced by deep braced excavations based on finite element method (FEM) and metaheuristic algorithms. Accordingly, a total of 1120 finite elements were analyzed to investigate the behaviors of DWD. Finally, a neural network with multiple layer perceptron (MLP) was optimized by two metaheuristic algorithms for predicting DWD, including whale optimization (WO) and Harris hawks optimization (HHO), called MLP-HHO and MLP-WO, respectively. The results indicated that the proposed MLP-HHO and MLP-WO provided high accuracy in predicting DWD. A comparison of the proposed models in this study and previous studies was also discussed to highlight the superiority of the proposed MLP-HHO and MLP-WO models.

Multi-type skin diseases classification using OP-DNN based feature extraction approach.

In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is tremendously hard because of the complexities like skin tone and color variation due to the presence of hair regions. Therefore the type of skin disease prediction is not accurately achieved in many pieces of research. To deal with mentioned concerns, a novel optimal probability-based deep neural network is proposed to assist medical professionals in appropriately diagnosing the type of skin disease. Initially, the input dataset is fed into the pre-processing stage, which helps to remove unwanted contents in the image. Afterward, features extracted for all the pre-processed images are subjected to the proposed Optimal Probability-Based Deep Neural Network (OP-DNN) for the training process. This classification algorithm classifies incoming clinical images as different skin diseases with the help of probability values. While learning OP-DNN, it is essential to determine the optimal weight values for reducing the training error. For optimizing weight in OP-DNN structure, an optimization approach is implemented in this research. For that, whale optimization is utilized because it works faster than other methods. The proposed multi-type skin disease prediction model is implemented in MatLab software and achieved 95% of accuracy, 0.97 of specificity, and 0.91 of sensitivity. This exposes the superiority of the proposed multi-type skin disease prediction model using an effective OP-DNN based feature extraction approach to attain a high accuracy rate and also it predict several kinds of skin disease than the previous models, which can protect the patients survives as well as can assist the physicians in making a decision certainly.

Fazi upravljanje optimizovano algoritmom afričkih supova za praćenje putanje zahvatnog mehanizma sa dva segmenta

This paper presents the proportional-derivative fuzzy controller for trajectory tracking of the gripping mechanism with two degrees of freedom. Aiming to achieve movement of the gripping mechanism without sudden starting and stopping, a polynomial velocity profile is utilized. The African vultures optimization, as one of the latest metaheuristic algorithms, is used to obtain the optimal input/output scaling gains of the proposed fuzzy controller according to the selected fitness function. The results obtained by this algorithm are compared with the other three new and popular metaheuristic algorithms: the whale optimization, the ant lion optimization and the sine cosine algorithm. Moreover, a simulation study was done for the defined initial position and for the scenario where there is a certain deviation because the gripping mechanism is not at its original initial position. Finally, the robustness of the controller is tested for the case when the masses of the segments increase three times. The results revealed that the suggested controller was capable of dealing with nonlinearities of the gripping mechanism, initial position and parameter changes. The movement of the gripping mechanism is smooth and follows the defined trajectory.

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Application Programming Interface (API) call feature analysis is the prominent method for dynamic android malware detection. Standard benchmark android malware API dataset includes features with high dimensionality. Not all features of the data are relevant, filtering unwanted features improves efficiency. This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance. In the first phase fuzzy benchmarking is used to select the top best features, and in the second phase meta-heuristic optimization algorithms viz., Moth Flame Optimization (MFO), Multi-Verse Optimization (MVO) & Whale Optimization (WO) are run with Machine Learning (ML) wrappers to select the best from the rest. Five ML methods viz., Decision Tree (DT), Random Forest (RF),K-Nearest Neighbors (KNN), Naïve Bayes (NB) & Nearest Centroid (NC) are compared as wrappers. Several experiments are conducted and among them, the best post reduction accuracy of 98.34% is recorded with 95% elimination of features. The proposed novel method outperformed among the existing works on the same dataset.

Performance analysis of doubly-fed induction generator using PID controller optimised by whale optimisation

Performance analysis of doubly-fed induction generator using PID controller optimised by whale optimisation

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

Social behavioral biometrics investigates social interactions to determine a person’s identity. Within the discipline of social behavioral biometrics, recognition of individuals based on their aesthetic preferences is an emerging direction of research. Human aesthetic is a soft, behavioral biometric trait that refers to a person’s attitudes towards a particular subject material. Recent developments in aesthetic-based biometric systems have proven that an individual’s visual and audio aesthetic preferences hold considerable distinctive features. This paper introduces a novel three-stage audio-aesthetic system that can uniquely identify a user from the set of their favorite songs. The system utilizes Residual Network (ResNet) for high-level feature extraction. A hybrid meta-heuristic feature selection algorithm based on Cuckoo Search and Whale Optimization is proposed for feature extraction optimization, which results in the low-dimensional feature set. The selected subset of features is fed into the XGBoost classifier to establish a person’s identity. The proposed method outperformed the handcrafted feature-based method by achieving 99.54% accuracy on a proprietary dataset (Free Music Archive) and 99.79% accuracy on a publicly available dataset (Million Playlists Dataset).

Intelligent Wheat Types Classification Model Using New Voting Classifier

When assessing the quality of the grain supply chain's quality, it is essential to identify and authenticate wheat types, as this is where the process begins with the examination of seeds. Manual inspection by eye is used for both grain identification and confirmation. High-speed, low-effort options became available thanks to automatic classification methods based on machine learning and computer vision. To this day, classifying at the varietal level is still challenging. Classification of wheat seeds was performed using machine learning techniques in this work. Wheat area, wheat perimeter, compactness, kernel length, kernel width, asymmetry coefficient, and kernel groove length are the 7 physical parameters used to categorize the seeds. The dataset includes 210 separate instances of wheat kernels, and was compiled from the UCI library. The 70 components of the dataset were selected randomly and included wheat kernels from three different varieties: Kama, Rosa, and Canadian. In the first stage, we use single machine learning models for classification, including multilayer neural networks, decision trees, and support vector machines. Each algorithm's output is measured against that of the machine learning ensemble method, which is optimized using the whale optimization and stochastic fractal search algorithms. In the end, the findings show that the proposed optimized ensemble is achieving promising results when compared to single machine learning models.

Neural Network Topology-Based Terahertz Absorber Using Fractal Frequency Selective Surface

This work offers a new kind of polarization-insensitive, angular stable, tunable fractal frequency selective surface (FSS) based terahertz (THz) absorber. An equivalent circuit model (ECM) of the absorber is developed and impedance is obtained analytically. Further, an ECM-backed artificial neural network (ANN) approach is proposed and used to optimize the absorber to the desired frequency and bandwidth. The minimum mean square error between targeted and observed outputs is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.86\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . The maximum absorption of 99% is achieved at a central frequency of 1.5 THz. The tunability is accomplished by varying the chemical potential ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu _{c}$ </tex-math></inline-formula> ) of the graphene layer introduced in the proposed absorbing structure. The resonance frequency shifts to 1.66 THz from 1.39 THz with a change in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu _{c}$ </tex-math></inline-formula> from 0.3eV to 0.6eV.The dual-band absorption peaks are noticed with fractal FSS based absorber within the operating frequency range of 0.1-4.0 THz. The maximum absorption of 0.99 and 0.98 at the resonant frequency of 1.0 THz and 3.1 THz with fractal FSS, respectively is achieved. Furthermore, the designed absorber works well for both perpendicular and parallel polarizations up to 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> angle of incidence. Moreover, the effect of structural parameters is also observed on the equivalent inductance and capacitance to understand the physics behind the structure. All the results obtained from ANN are verified with particle swarm optimization (PSO) and Whale optimization (WO) techniques. Good congruence among ANN, PSO, and WO results shows the effectiveness of the aforementioned technique. The possible applications of the proposed tunable absorber are THz imaging, sensing, detection, and stealth.

Galactic swarm‐improved whale optimization algorithm‐based resource management in Internet of things

SummaryInternet of things (IoT) is the reliable alternative among the networking technologies for achieving high performance with improved potentialities of flexible adoptions, data exchanges, resource allocations, and system controls. The existing IoT suffers from the limitations of resource allocation ranging between complicated service provisioning environments and networking service quality mismatching. IoT environment needs to handle the resource allocation issue for attaining satisfactory degree of quality of experience (QoE) that maps multiple resources to gateways. This problem of mapping multiple resources to gateways belongs to the class of NP‐complete problem and can be ideally solved through intelligent metaheuristic algorithms. In this paper, galactic swarm‐improved whale optimization algorithm‐based resource management (GSIWOA‐RM) scheme is proposed for efficient mapping of multiple resources to gateways in IoT. It specifically utilizes galactic swarm optimization algorithm (GSOA) for establishing global control with inherited multiple adaptive cycles of exploitation and exploration during resource allocation. It further prevents the limitations of early convergence in the exploitation phase by utilizing the evolution‐based whale optimization algorithm (WOA) that aids in better balance between exploitation and exploration. The simulation results of the proposed GSIWOA‐RM scheme confirm a better throughput of 28.32% with minimized delay and energy consumptions of 19.24% and 21.82%, when compared to the baseline resource management schemes.