Enhanced Whale Optimization Algorithm Research Articles

Enhanced Whale Optimization Algorithm for task scheduling in cloud computing environments

The escalation of cloud services, driven by their accessibility, improved performance, and cost-effectiveness, has led cloud service providers to consistently seek methods to expedite job completion, thereby boosting profits and reducing energy consumption expenses. Despite developing numerous scheduling algorithms, many of these techniques address only a specific objective within the scheduling process. To efficiently achieve better optimization results for the cloud task scheduling problem, a novel approach, the Enhanced Whale Optimization Algorithm (EWOA), is proposed. EWOA integrates the WOA with the Lévy flight. The incorporation of Lévy flight is tailored to broaden the search space of WOA, expediting convergence with adaptive crossover. The EWOA model is simulated using the Cloudsim tool and evaluated under diverse test conditions. The effectiveness of EWOA is assessed by employing various parameters and comparing them with existing algorithms. The results demonstrate that EWOA outperforms other algorithms in resource utilization, energy consumption, and execution cost, establishing its superiority in addressing the complexities of multi-objective cloud task scheduling.

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

Enhanced Whale Optimization Algorithm for Fuzzy Proportional–Integral–Derivative Control Optimization in Unmanned Aerial Vehicles

The traditional PID controller in quadrotor UAVs has poor performance, a large overshoot, and a long adjustment time, which limit its stability and accuracy in practical applications. In order to solve this problem, an improved whale optimization fuzzy PID control strategy based on CRICLE chaos map initialization is proposed, and a detailed simulation analysis was carried out using MATLAB software (MATLAB R2022B). Firstly, to more realistically reflect quadrotor UAVs’ flight behavior, a dynamic simulation model was established, and the dynamics and kinematic characteristics of the aircraft were considered. Then, CRICLE chaotic mapping initialization was introduced to improve the global search ability of the whale optimization algorithm and to effectively initialize the parameters of the fuzzy PID controller. This improved initialization method helped to speed up the convergence process and improve the stability of the control system. In the simulation experiments, we compared the performance indicators of the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID controller to the traditional PID and fuzzy PID controllers, including overshoot, adjustment time, etc. The results show that the proposed control strategy has better performance than the traditional PID and fuzzy PID controllers, significantly reduces overshoot, and achieves a significant improvement in adjustment time. Therefore, the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID control strategy proposed in this study provides an effective solution for improving the performance of the quadrotor control system and has practical application potential.

Enhanced whale optimization algorithm for dependent tasks offloading problem in multi-edge cloud computing

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape.

Optimization method for underwater sensor networks based on a virtual force‐oriented enhanced whale optimization algorithm

AbstractThis paper presents an enhanced whale optimization algorithm based on virtual force to optimize coverage and address the problem of uneven coverage during the deployment of underwater sensor networks. This method is guided by the Lévy flight and virtual force algorithms and adopts a differential mutation strategy based on random walks. This improves the fitness of the initial population and population richness of the algorithm. The effectiveness of this method was verified through simulation.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition. However, due to unknown model parameters such as the mass, moment of inertia and mechanical size, the dynamic model of exoskeletons is difficult to construct. Hence, an enhanced whale optimization algorithm (EWOA) is proposed to identify the exoskeleton model parameters. Meanwhile, the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints. Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion. Finally, the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform. The knee joint motion achieves a steady-state response after 0.5 s. Meanwhile, the position error of hip joint response is less than 0.03 rad after 0.9 s. In addition, the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{N}\\cdot\ ext{m}}$$\\end{document}. This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters. Furthermore, an enhanced mutation strategy is adopted to avoid whale evolution's unsatisfactory local optimal value.

A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm

Cybersecurity is critical in today’s digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people’s protection. One type of cyberattack known as “credential stuffing” involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

A hybrid CNN–BiLSTM–AT model optimized with enhanced whale optimization algorithm for remaining useful life forecasting of fuel cell

To further improve the remaining useful life forecasting accuracy of fuel cells using classic deep learning models, a convolutional neural network combining bidirectional long and short-term memory networks (BiLSTM) and attention mechanism (AT) is optimized with the enhanced whale optimization algorithm (EWOA). Singular spectrum analysis preprocesses the attenuation data to eliminate noise and enhance its effective information; the CNN–BiLSTM model extracts spatiotemporal features and learns historical and future information; AT further explores the spatiotemporal correlation; and EWOA optimizes its hyperparameters to reduce human intervention error. Results demonstrate that, compared with long and short-term memory, CNN–LSTM, CNN–BiLSTM, CNN–BiLSTM–AT, and CNN–BiLSTM–AT optimized with other algorithms, the CNN–BiLSTM–AT model optimized with EWOA achieves lower root mean square error, mean absolute error, mean absolute percentage error, and relative errors of 0.1951%–0.2059%, 0.1267%–0.1538%, 0.0319%–0.0366%, and 0.026%–0.036%, respectively, with different training data. Importantly, the proposed model still maintains good prediction robustness with over 40% of the missing data.

Bi-level optimization configuration method for microgrids considering carbon trading and demand response

Under the background of the shortage of traditional energy and the increasingly serious environmental problems, this paper proposes a bi-level optimization configuration method for microgrids based on stepped carbon trading and price-based demand response. The stepped carbon trading mechanism is introduced into the planning model to clarify the impact of construction processes on land carbon emissions. Simultaneously, price-based demand response during system operation is introduced, guiding users to change the electricity consumption strategies to promote the consumption of renewable energy. Then, a bi-level optimal configuration framework is constructed. The upper level is optimized to allocate the capacity of the microgrid, with the lowest annual equivalent comprehensive cost as the objective function. The lower level is optimized for microgrid operation, with the minimum sum of the microgrid operation cost and carbon emission cost as the objective function. In view of the advantages of enhanced whale optimization algorithm (E-WOA) in solving multi-dimensional feature selection problems, E-WOA and CPLEX solver are combined to solve the bi-level optimization model for the first time. Simulation results show that after introducing a stepped carbon trading mechanism, the system carbon emissions decreased by 23.09%. Considering price-based demand response under the premise of introducing a stepped carbon trading mechanism, the total cost decreased by 3.57% and the carbon emissions decreased by 19.83%.

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Optimal coding unit decision for early termination in high efficiency video coding using enhanced whale optimization algorithm

Video compression is an emerging research topic in the field of block based video encoders. Due to the growth of video coding technologies, high efficiency video coding (HEVC) delivers superior coding performance. With the increased encoding complexity, the HEVC enhances the rate-distortion (RD) performance. In the video compression, the out-sized coding units (CUs) have higher encoding complexity. Therefore, the computational encoding cost and complexity remain vital concerns, which need to be considered as an optimization task. In this manuscript, an enhanced whale optimization algorithm (EWOA) is implemented to reduce the computational time and complexity of the HEVC. In the EWOA, a cosine function is incorporated with the controlling parameter A and two correlation factors are included in the WOA for controlling the position of whales and regulating the movement of search mechanism during the optimization and search processes. The bit streams in the Luma-coding tree block are selected using EWOA that defines the CU neighbors and is used in the HEVC. The results indicate that the EWOA achieves best bit rate (BR), time saving, and peak signal to noise ratio (PSNR). The EWOA showed 0.006-0.012 dB higher PSNR than the existing models in the real-time videos.

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

From the perspective of energy efficiency and environmental sustainability, the scheduling problem in a flexible workshop with the utilization of automated guided vehicles (AGVs) was investigated for material transportation. Addressing the dual-constrained integrated scheduling challenge involving machining machines and AGVs, a scheduling optimization model was established with makespan, workshop energy consumption, and processing quality as the optimization objectives. To effectively solve this model, an enhanced whale optimization algorithm (IWOA) was proposed. Specifically, nonlinear convergence factors, adaptive inertia weights, and improved helix positions were introduced into the standard whale optimization algorithm to update the model. Furthermore, a loss function was constructed based on fuzzy membership theory to obtain the optimal compromise solution of the multi-objective model. The research results indicate that: (1) The IWOA obtained the optimal solutions on benchmark instances MK01, MK02, MK04, MK07, and MK08; (2) The IWOA outperformed the WOA(1), WOA(2), WOA-LEDE, and NSGA-II algorithms in the two instances provided in this paper, demonstrating strong robustness of the model; (3) Although the multi-objective model constructed in this paper could not surpass the single-objective optimal solution in individual objectives, it achieved compensation in other objectives, effectively balancing the trade-offs among the makespan, workshop energy consumption, and processing quality of the three objectives. This research offers an effective practical approach to address green flexible workshop scheduling with AGV transportation.

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion—a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

Enhanced Whale Optimization Algorithm with Wavelet Decomposition for Lithium Battery Health Estimation in Deep Extreme Learning Machines

Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method’s excellent accuracy and robustness.

Remaining useful life prediction of lithium-ion batteries based on the Support Vector Regression Optimized and Enhanced Whale Optimization Algorithm

Accurate and reliable predictions of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the proper functioning and longevity of lithium-ion battery systems. Predicting the RUL in its early stages can be challenging and difficult for conventional RUL prediction techniques. To tackle this challenge, this paper proposes a novel approach that integrates five carefully chosen health features with a Support Vector Regression Optimized model and Enhanced Whale Optimization Algorithm (EWOA-SVR) to predict the future capacity trend and subsequently estimate the RUL using XGBoost method according to the predicted capacity. The proposed approach aims to achieve early and accurate capacity estimation and RUL prediction for lithium-ion batteries. To begin, this method extracts some health features during the relaxation process and during the constant-current and constant-voltage charge (CC-CV) process. To demonstrate the strength of the correlation between the health features and capacity, both Pearson and Spearman rank correlation analysis are employed in this study. Then based on the measurable health features and faded capacity data, Ant Lion Optimization (ALO), Whale Optimization Algorithm (WOA) and EWOA are used to select suitable penalty factor and kernel parameter in the SVR algorithm to improve the performance of SVR. With the well-trained model, the future capacity can be predicted with an accuracy of MAPE within 1%, and the RUL prediction error is lower than 11 cycles. The results suggest that the proposed model can achieve a precise and dependable early estimation of the future capacity and RUL prediction.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor.

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed.

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.