Modified Whale Optimization Algorithm Research Articles

Multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted mobile edge computing with heterogeneous requirements

In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneous task requirements. In the framework, the task delay and the number of successfully executed tasks are optimized by jointly controlling the trajectories of multiple UAVs. To address the considered trajectory planning optimization problem, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm is proposed, where each UAV is regarded as a learning agent. First, a counterfactual inference based personalized policy update mechanism is proposed to evaluate the independent policy of agents by comparing the policy with a designed counterfactual policy. Based on this idea, each agent updates a personalized policy from both group and individual interests to improve its cooperation ability in dynamic and complex environments. Then, a diversified experience sampling mechanism is proposed to enhance the efficiency of policy evaluation and update with rich experiences provided by the environment interaction and the modified whale optimization algorithm. Finally, evaluation results demonstrate the superiority and effectiveness of the proposed MADRL algorithm.

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm

This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air quality deterioration poses significant risks to both human health and ecological equilibrium, pinpointing relevant features becomes essential for boosting prediction accuracy. The scope of the research includes comparing the performance of mWOA with the original WOA in two feature selection techniques: filter-based and wrapper-based. The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. The statistical results demonstrate the effectiveness of the proposed mWOA approach, outperforming WOA due to the modified control parameter that enables a more precise exploration of the search area. The findings of this study reveal the improved performance of mWOA in selecting informative features, resulting in better prediction on average: 93.75% for filter-based and 94.49% for wrapper-based. In conclusion, the wrapper-based feature selection using the mWOA approach proves to be a valuable asset in enhancing the accuracy and efficiency of ozone level detection models. In the future, the proposed technique can be used for more applications in environmental science and engineering research.

Active Disturbance Rejection Control Design with Sensitivity Constraint for Drum Water Level

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes.

Global optimization of optimal Delaunay triangulation with modified whale optimization algorithm

Global optimization of optimal Delaunay triangulation with modified whale optimization algorithm

Smoothness-oriented motion planning method for milling robot considering workpiece set-up optimization

Robots are attracing increasing attention in the machining field owing to their great flexibility, vast workspace and low cost. Functional redundancy exists when robots are engaged in milling tasks. Redundancy resolution be considered not only in milling robot motion planning, but also in workpiece layout. A two-step motion planning method is proposed to optimize the trajectory and the workpiece set-up simultaneously to improve planning efficiency with less computational burden. In the first stage, a bilevel optimization technique is proposed that can plan the motion from a global perspective and search the optimal workpiece layout. In the second step, based on a novel smoothness index, a modified whale optimization algorithm combined with the differential evolution algorithm (DE-WOA) is proposed to resolve redundancy under robot and machining error constraints. Experiments demonstrate that the proposed method can enhance the quality of surface machining while avoiding the impact of path length on computational complexity.

Malware cyberattacks detection using a novel feature selection method based on a modified whale optimization algorithm

Malware cyberattacks detection using a novel feature selection method based on a modified whale optimization algorithm

Alleviation and control of chaotic oscillations in SMIB power systems using a modified-Whale optimization-based battery-STATCOM

BackgroundChaotic oscillations within the power system give rise to instability. While these oscillations may not have an immediate impact on the synchrony of the machine, they stimulate one of the oscillation modes, ultimately leading to voltage collapse and a loss of synchronism. ObjectiveThis paper introduces a modified Whale Optimization Algorithm (WOA)-based Battery-STATCOM (Static Synchronous Compensator) as a solution to mitigate chaotic oscillations within a Single Machine Infinite Bus (SMIB) system. MethodologyAn adaptable controller is implemented to manage the gate signal within the Battery-STATCOM. The AC-DC currents of this controller are optimally governed by two distinct WOA-tuned Proportional-Integral (PI) controllers. The battery storage unit serves as a robust voltage source, with the intelligent controller maintaining the DC-link voltage at the desired level. Test CasesAdditional disturbances, such as gradual variations in reference voltage and electromagnetic torque, are introduced to exacerbate chaotic oscillations. This is done to assess the controller's real-world performance under adverse conditions. Results and ConclusionUnder zero damping conditions, rotor parameters, including rise time, settling time, peak time, and overshoot, initially remain undefined due to uncontrolled oscillations. However, once the Battery-STATCOM is applied, these parameters are defined and achieve values (in seconds) of 0.90, 6.21, 1.71, and 21.10, respectively. After further optimization through the proposed modified WOA optimizer, the parameters reach values of 0.25, 1.01, 0.89, and 1.78, respectively. These results underscore the effectiveness of the proposed metaheuristic controller in suppressing overall chaotic oscillations within the power system.

Metaheuristic Algorithms for Evaluating the Effect of Electric Vehicle Charging Infrastructures in Distribution Networks

To address the effect of electric vehicle (EV) load on distribution system efficient optimization techniques are required. In this paper, an attempt is made to work with various new optimization techniques for minimizing the detrimental effect of EV charging station (EVCS) load on the electrical network. The operating conditions of the distribution network, that is, voltage stability, reliability and power loss (VRP) index are optimized in this work in a framework of multiple objectives with various technical constraints. Although many optimization approaches have been developed in recent years, eight well-known techniques are employed in the present work such as modified teaching-learner-based optimization (MTLBO), JAYA, modified JAYA (MJAYA), ant–lion optimization (ALO), whale optimization technique (WOT), grasshopper optimization technique (GOT), modified whale optimization algorithm (MWOA), and hybrid whale particle swarm optimization (HWPSOA). All the eight techniques’ performance is compared under two different cases of operations by testing the objective function on the modified IEEE 33 bus distribution system using MATLAB software. The numerical solutions obtained by the HWPSOA indicate the suitability and effectiveness for optimizing the specified complex multi-objective function compared to other techniques.

A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems

In order to better understand and analyze population-based meta-heuristic optimization algorithms, this paper proposed a new hybrid algorithm combined Lévy flight with modified Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , which is called LMWOAGWO to discard the dross and select the essence. Firstly, the population is initialized by using the uniform distribution space combined with the pseudo-reverse learning strategy, which lays the foundation for global search. Then, modifications were made to both WOA and GWO. For WOA algorithm, random adjustment control parameters strategy and different chaotic maps are used to adjust the main parameters of WOA to avoid the algorithm falling into local optimum in the later stage. For GWO algorithm, a new optimal solution is added to the grey wolf population to increase the optimal update position of the algorithm. On this basis, the dynamic weighting strategy is introduced to improve the convergence accuracy and convergence speed of the algorithm. Subsequently, new conditions were added during the WOA exploitation phase to formulate LMWOAGWO and the greedy strategy is used to retain better iteration update locations. Finally, Lévy flight is used to improve the global search ability of the algorithm. Extensive numerical experiments were conducted using 23 standard test benchmark functions, 25 CEC2005 functions, 15 popular benchmark functions and 10 CEC2019 functions to test the performance of LMWOAGWO compared with other well-known swarm optimization algorithms. Experimental and statistical results show that the performance of LMWOAGWO algorithm is better than many state-of-the-art algorithms. Then, 22 real-world optimization problems were used to further study the effectiveness of LMWOAGWO. Winners of CEC2020 Real World Single Objective Constraint Optimization Competition, such as iLSHADEϵ algorithm, sCMAgES algorithm, COLSHADE algorithm and EnMODE algorithm are selected as four comparison algorithms in real world optimization problems. Experimental results show that the proposed LMWOAGWO has the capability to solve real-world optimization problems. Finally, the application efficiency of LMWOAGWO in solving two basic optimization problems in wireless networks is briefly introduced, and compared with the original WOA and GWO. Simulation results show that the performance of the LMWOAGWO is better than WOA and GWO.

Drone Powered by Solar Energy and Whale Optimization Algorithm

Drone Powered by Solar Energy and Whale Optimization Algorithm

An Effective Approach for Determining Rock Discontinuity Sets Using a Modified Whale Optimization Algorithm

An Effective Approach for Determining Rock Discontinuity Sets Using a Modified Whale Optimization Algorithm

Improved super-resolution perception convolutional neural network for photovoltaics missing data recovery

Errors in data measurement and recording can induce data missing for photovoltaic (PV) operation. Flawed data, however, can greatly impair the performance of data-driven models such as the accuracy of PV generation forecasting. The super-resolution perception convolutional neural network (SRPCNN) is a powerful tool in data reconstruction from low frequency to high frequency and it can be used for data recovery. The SRPCNN, however, has problems in dealing with null values for PV data recovery. To solve this problem, an improved SRPCNN model, which integrates naïve SPRCNN and linear interpolation, is proposed in this paper. Experiments are carried out under 5 different missing rates. The improved model can handle null values better, and can have a better accuracy improvement than the naïve SPRCNN and other methods. To further improve the data recovery performance, a modified whale optimization algorithm is deployed to optimize the hyperparameters of SPRCNN. To verify the efficiency of the proposed model, extensive numerical experiments on different types of data from an actual PV power plant have been conducted by comparing with benchmarks. Compared with the naïve SRPCNN, the RMSE and MAE of the proposed model are reduced by more than 30% in various cases. Results show that the improved SRPCNN model is efficient in PV data recovery with various missing rates.

Energy cost optimization in distribution network considering hybrid electric vehicle and photovoltaic using modified whale optimization algorithm

Energy cost optimization in distribution network considering hybrid electric vehicle and photovoltaic using modified whale optimization algorithm

Optimal control for a photovoltaic integrated grid system using PSO and modified whale optimization to enhance power quality

The paper proposes a modified & optimized controller for PV-integrated grid-connected PV systems. As the solar power output depends on the environmental conditions, i.e., irradiation and atmospheric temperature, the maximum power from the photovoltaic (PV) module is extracted using perturb and observe (P & O) algorithm. With a variety of controllers, such as conventional proportional and integral (PI) controllers, particle swarm optimization (PSO), and the modified Whale optimization algorithm (MWOA) method based on optimum control, the power as compared to extracted from PV panels using MWOA-based controllers. A complete simulation and implementation of a single-phase grid-connected inverter are shown to verify the proposed PV controller. The power flow control and grid synchronization are also presented when the above controllers act as MPPT controllers in the proposed system. The performance of proposed system is evaluated and compared among conventional and optimal controllers under a MATLAB-Simulink environment.

Implementation of Modified Whale Optimization Algorithm in Two-Degree-of-Freedom Fractional Order–Fuzzy-ProportionalIntegral-Derivative Controller for Automatic Generation Control in a Multi-Area Interconnected Power System

Implementation of Modified Whale Optimization Algorithm in Two-Degree-of-Freedom Fractional Order–Fuzzy-ProportionalIntegral-Derivative Controller for Automatic Generation Control in a Multi-Area Interconnected Power System

The research of a novel WOG-YOLO algorithm for autonomous driving object detection

Object detection has been one of the critical technologies in autonomous driving. To improve the detection precision, a novel optimization algorithm is presented to enhance the performance of the YOLOv5 model. First, by improving the hunting behavior of the grey wolf algorithm(GWO) and incorporating it into the whale optimization algorithm(WOA), a modified whale optimization algorithm(MWOA) is proposed. The MWOA leverages the population’s concentration ratio to calculate p_h for selecting the hunting branch of GWO or WOA. Tested by six benchmark functions, MWOA is proven to possess better global search ability and stability. Second, the C3 module in YOLOv5 is substituted by G-C3, and an extra detection head is added, thus a highly optimizable detection G-YOLO network is constructed. Based on the self-built dataset, 12 initial hyperparameters in the G-YOLO model are optimized by MWOA using a score fitness function of compound indicators, thus the final hyperparameters are optimized and the whale optimization G-YOLO (WOG-YOLO) model is obtained. In comparison with the YOLOv5s model, the overall mAP increases by 1.7%, the mAP of pedestrians increases by 2.6% and the mAP of cyclists increases by 2.3%.

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

In recent decades, nature-inspired optimization methods have played a critical role in helping industrial plant designers to find superior solutions for process parameters. According to the literature, such methods are simple, quick, and indispensable for saving time, money, and energy. In this regard, the Modified Whale Optimization Algorithm (MWOA) hybridized with Artificial Neural Networks (ANN) has been employed in the Reverse Osmosis (RO) desalination plant performance to estimate the permeate flux (0.118‒2.656 L/h m2). The plant’s datasets have been collected from the literature and include four input parameters: feed flow rate (400‒600 L/h), evaporator inlet temperature (60‒80 °C), feed salt concentration (35‒140 g/L) and condenser inlet temperature (20‒30 °C). For this purpose, ten predictive models (MWOA-ANN Model-1 to Model-10) have been proposed, which are capable of predicting more accurate permeate flux (L/h m2) than the existing models (Response Surface Methodology (RSM), ANN and hybrid WOA-ANN models) with minimum errors. Simulation results suggest that the MWOA algorithm demonstrates a stronger optimization capability of finding the correct weights and biases so as to enable superior ANN based modeling without limitation of overfitting. Ten MWOA-ANN models (Model-1 to Model-10) have been proposed to investigate the plant’s performance. Model-6 with a single hidden layer (H = 1), eleven hidden layer nodes (n = 11) and the thirteen search agents (SA = 13) produced most outstanding regression results (R2 = 99.1%) with minimal errors (MSE = 0.005). The residual errors for Model-6 are also found to be within limits (span of − 0.1 to 0.2). Finally, the findings show that the screened MWOA-ANN models are promising for identifying the best process parameters in order to assist industrial plant designers.

A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

Hybrid improved capuchin search algorithm for plant image thresholding.

With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.