Adaptive Whale Optimization Algorithm Research Articles

Adaptive Whale Optimization Algorithm–DBiLSTM for Autonomous Underwater Vehicle (AUV) Trajectory Prediction

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.

Advancing Slim-Hole Drilling Accuracy: A C-I-WOA-CNN Approach for Temperature-Compensated Pressure Measurements.

This paper presents a novel method to improve drill pressure measurement accuracy in slim-hole drilling within the petroleum industry, a sector often plagued by extreme conditions that compromise data integrity. We introduce a temperature compensation model based on a Chaotic-Initiated Adaptive Whale Optimization Algorithm (C-I-WOA) for optimizing Convolutional Neural Networks (CNNs), dubbed the C-I-WOA-CNN model. This approach enhances the Whale Optimization Algorithm (WOA) initialization through chaotic mapping, boosts the population diversity, and features an adaptive weight recalibration mechanism for an improved global search and local optimization. Our results reveal that the C-I-WOA-CNN model significantly outperforms traditional CNNs in its convergence speed, global searching, and local exploitation capabilities, reducing the average absolute percentage error in pressure parameter predictions from 1.9089% to 0.86504%, thereby providing a dependable solution for correcting temperature-induced measurement errors in downhole settings.

Toward Smart Traffic Management With 3D Placement Optimization in UAV-Assisted NOMA IIoT Networks

Next generation networks will involve huge number of industrial internet of things (IIoT) sensors which require reliable connectivity with low latency to manage the data transmission and processing. The design of these networks entails a lot of challenges. This article describes the 3D placement of multiple unmanned aerial vehicles (UAVs) in an IIoT network that supports non-orthogonal multiple access (NOMA). UAVs act as decode and forward (DF) relays. The 3D UAV placement problem is formulated which is highly non-convex in the coordinates. Therefore, we employ an improved adaptive whale optimization algorithm (IAWOA) to handle the problem. Even with its improved performance, IAWOA is not suitable for real-time application. Hence, we propose path aggregation network (PANet) to handle the 3D UAV placement. The simulation results show that PANet is more suitable for the online-learning.

A synergy of the adaptive whale optimization algorithm and differential evolution for abrupt motion tracking

Aiming at the problem that conventional trackers are difficult to adapt to abrupt motion, a tracking algorithm based on a synergy of the adaptive whale optimization algorithm and differential evolution (AWOA-DE) is proposed in this paper. Firstly, the non-linear dynamic feedback strategy based on the one-fifth principle is employed to replace the linear mechanism, which is the variation of exploring step in the basic whale optimization algorithm (WOA), and an adaptive WOA (AWOA) is generated to improve the global optimization ability of the WOA. Secondly, AWOA adopts a random sampling strategy to generate new samples, which could ensure the diversity of solutions, but may lead to instability. By mixing AWOA and DE algorithms, the population is divided into two groups, which are updated by AWOA and DE algorithms respectively, to ensure the diversity of the populations and improve the optimization accuracy of the algorithm. Finally, a visual tracker based on AWOA-DE is proposed, which can deal with the problem of abrupt motion in tracking. In addition, the effectiveness of the hybrid optimization algorithm is evaluated using 23 benchmark functions. In the experiment of visual tracking, extensive experimental results show that the AWOA-DE tracker (AWOADET) has much stronger competitiveness compared to the WOA tracker (WOAT) and the other 10 state-of-the-art trackers, especially for abrupt motion tracking.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem is formulated as a mixed-integer-and-nonlinear programming (MINLP), which is challenging to address by the traditional solution, because the solution may easily fall into the local optimal. To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.

A multistrategy hybrid adaptive whale optimization algorithm

Abstract To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) was proposed. In this algorithm, the logistic–tent hybrid chaos algorithm was used to initialize the population, which could make the population distribution more random and uniform. The opposition-based learning strategy was adopted to expand the population of individuals and complete the population selection. To balance the exploitation phase and exploration phase, a dynamic parameter was constructed based on the sigmoid excitation function, and an active adaptive weight was added to adjust the global and local search, which accelerated the convergence speed also. The perturbation mechanism of the Student T-distribution was introduced with random perturbation to expand the search range and improve the global search ability of the algorithm. In total, 23 benchmark functions were selected to conduct convergence performance and optimization performance experiments of the proposed algorithm. The average value and standard deviation were determined as evaluation indexes. The MHWOA was compared with other improved WOA variants and advanced algorithms. The results showed that the proposed MHWOA had better iterative convergence and optimization performance than different algorithms on the optimization of unimodal functions, multimodal functions, and fixed dimension functions. Meanwhile, the MHWOA was applied to the optimal designs of pressure vessels and springs. The experimental results displayed that the MHWOA obtained better solutions than other meta-heuristic algorithms. This study has practical solid application value, which can be applied to solving various engineering problems.

Multistrategy Improved Whale Optimization Algorithm and Its Application.

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

Deep Learning-Based Model for Financial Distress Prediction.

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

IRI: An intelligent resistivity inversion framework based on fuzzy wavelet neural network

To acquire high-quality resistivity inversion results, an intelligent resistivity inversion (IRI) framework based on a fuzzy wavelet neural network (FWNN) is proposed in this paper. In the IRI framework, first, the FWNN is applied to build an interpretable inversion model for analyzing the apparent resistivity data. Takagi–Sugeno–Kang (TSK) fuzzy model is introduced to FWNN to explain the rules of the inversion results, and the wavelet neural network (WNN) is applied to construct the consequent part for each fuzzy rule and enhance the high-order nonlinear fitting ability of TSK fuzzy model. Then, to enhance the generalization and robustness, an elastic gradient descent (EGD) method is designed, which is used to update the linear parameters of the FWNN. Next, a differential and adaptive whale optimization algorithm (DAWOA) is introduced to train the nonlinear parameters of the FWNN for avoiding local optimum. Moreover, in the proposed DAWOA, a differential foraging strategy and an adaptive predation strategy are introduced to improve global exploration and local exploitation. All these measures can improve the inversion accuracy and accelerate the training process of FWNN for ERI inversion. Several simulation results demonstrate the feasibility and applicability of the IRI framework.

Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment

Cloud computing is the computing technology that offers dynamically scalable and flexible computing resources. Task scheduling in the cloud system is the major problem that needs to be tackled for enhancing the system performance and cloud customer satisfaction level. The task scheduling scheme directly affects the execution time as well as the execution cost of the system. To overcome the above-stated issue, the novel hybrid Whale optimization algorithm-based MBA algorithm is proposed for solving the multi-objective task scheduling problems in cloud computing environments. In the hybrid WOA based MBA algorithm, the multi-objective behavior decreases the makespan by maximizing the resource utilization. The output of the Random double adaptive whale optimization algorithm (RDWOA) is enhanced by utilizing the mutation operator of the Bees algorithm. The performance evaluation is conducted and compared with other algorithms using the platform of Cloudsim tool kit for various measures such as completion, time, and computational cost. The results are analyzed for the performance measures such as makespan, execution time, resource utilization and computational cost and the analysis proves that the proposed algorithm performs better than other algorithms such as IWC, MALO, BA-ABC and MGGS. The proposed HWOA based MBA algorithm converged faster than any other approach for large search spaces and makes it appropriate for large scheduling problems. The experimental results reveal that the HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.

Dominant color component and adaptive whale optimization algorithm for multilevel thresholding of color images

Optimal multilevel thresholding for image segmentation got much importance in recent years. Several entropic and non-entropic objective functions with evolutionary computing algorithms have been successfully implemented to get the optimal multilevel thresholds for gray scale images. The problem of multilevel thresholding becomes complex for color images. Because, the basic color components (red, blue, green) of the color image are extracted and the multiple optimum threshold values are calculated for each of the components separately. This makes the methods computationally intensive and inaccurate. Further, the required color information is not retained in the thresholded output. To solve these problems, an efficient technique is proposed in this paper, extracting only the dominant color component (DCC) of an image, for optimal thresholding. A novel segmentation score is introduced to justify the methodology. The optimum threshold values are obtained using a newly suggested evolutionary computing technique named adaptive whale optimization algorithm (AWOA). The main contributions are – (i) a novel DCC approach is introduced, (ii) an efficient optimizer AWOA is proposed, (iii) a new segmentation score is introduced, (iv) experimental results on standard test color images are explored. The outcomes are compared with all existing method’s approaches (using all the RGB components) on color image thresholding. Its performance analysis using standard metrics is deliberated in detail. Statistical analysis is also performed. From the outcomes, it is perceived that the suggested DCC-AWOA concept yields high quality segmented images. The work may encourage further research to explore its high dimensional applications.

Enhanced precision inspection of free-form surface with an improved whale optimization algorithm.

For precision inspection of free-form surface parts using non-contact measurement methods, the registration between the actual measurement model and the ideal design model is necessary.The traditional iterative closure point (ICP) method requires good initial parameters to obtain the global optimal transformation matrix, which is difficult to guarantee in the actual detection process. In order to improve the accuracy and robustness of free-form surface precision inspection, an Improved Whale Optimization Algorithm (IWOA) is proposed in this study.This algorithm can solve the required registration parameters by constantly updating the population. A measurement experimental system is designed to test the accuracy of blade registration. The performance of IWOA is evaluated by the actual measurement experiment, and the results are verified by a comparative study with Whale Optimization Algorithm (WOA), Lévy flight trajectory-based Whale Optimization Algorithm (LWOA), and Adaptive Whale Optimization Algorithm (AWOA). The surface registration errors are 0.1711mm for IWOA, 2.0015 mm for WOA, 1.2656 mm for LWOA, 2.8132 mm for AWOA and 2.1537 mm for ICP. The results show that the accuracy of IWOA is more than 7 times higher than other four algorithms. In general, the experiments indicate that IWOA has a good registration ability and can meet the needs of industrial measurement.

A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs

Routing requests in industrial wireless sensor networks (IWSNs) are always restricted by QoS. Therefore, finding a high-quality routing path is a key problem. In this paper, a clone adaptive whale optimization algorithm (CAWOA) is designed for reducing the routing energy consumption of IWSNs with QoS constraints, and a novel clone operator is proposed. More importantly, CAWOA innovatively adopts a discrete binary-based routing coding method, which provides strong support for optimal routing schemes. In addition, a novel routing model of IWSNs combined with QoS constraints has been designed, which involves comprehensive consideration of bandwidth, delay, delay jitter, and packet loss rate. Subsequently, in a series of simulations, the proposed algorithm is compared with other heuristic-based routing algorithms, namely, whale optimization algorithm (WOA), simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA). The simulation results suggest that the CAWOA-based routing algorithm outperforms other methods in terms of routing energy consumption, convergence speed, and optimization ability. Compared with GA, SA, PSO, and WOA under the conditions that the number of nodes is 120, the maximum delay is 120 ms, the maximum delay jitter is 25 ms, the maximum bandwidth is 9 Mbps, and the packet loss rate is 0.02; the energy consumption of CAWOA-based routing is reduced by 12%, 17%, 19%, and 7%, respectively.

Intelligent fault diagnosis of rolling bearings based on refined composite multi-scale dispersion q-complexity and adaptive whale algorithm-extreme learning machine

In order to extract the non-linear fault characteristics of rolling bearings more accurately, a novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale dispersion q-complexity (RCMSDQC), is proposed for fault feature extraction of rolling bearings. To improve further the overall performance of the extreme learning machine (ELM) algorithm, the adaptive whale optimization algorithm (AWOA) is used to determine the input weights and hidden layer biases of the ELM. The RCMSDQC has the advantages of strong feature extraction ability and stability compared to the composite multi-scale weighted permutation entropy (CMSWPE) and composite multi-scale permutation entropy (CMSPE) methods. Furthermore, compared to the whale optimization algorithm, particle swarm optimization, and genetic algorithm, the AWOA shows a superior performance in the benchmark function comparison experiment. Based on the experimental rolling bearing data from the Paderborn University, the performance of the proposed method is further evaluated. The experimental results indicate that the proposed fault diagnosis method can identify the type and severity of rolling bearing faults with an accuracy of 99.1%.

Simultaneous Reconfiguration and Optimal Capacitor Placement Using Adaptive Whale Optimization Algorithm for Radial Distribution System

The losses in the distribution networks due to the line resistance decrease the overall efficiency of the power distribution. Reducing the power losses and regulating the voltages within the limits are necessary to provide quality power to the consumers. The power loss can be minimized by optimum network reconfiguration and the placement of the capacitors. Considering independent network reconfiguration and placement of the capacitor is not effective during heavy loading conditions. This paper proposes a simultaneous network reconfiguration and capacitor placement in radial distribution network to minimize the real power losses, operating cost and to improve the bus voltages. The Johnson’s algorithm is used to find the minimal spanning tree during the network reconfiguration and an adaptive whale optimization algorithm is used as an optimization method. The proposed methodology is tested on standard IEEE 33-bus and 69-bus radial distribution systems. The effectiveness of the proposed method is validated by comparing the results with previous result reported in the literature in terms of cost saving and loss reduction.

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.

Optimal utilization of interconnected RESs to microgrid: a hybrid AWO-ANFIS technique

This dissertation proposes an elite converter for the optimal utilization of hybrid renewable energy sources in the microgrid-associated systems using hybrid technique. Microgrid (MG)-associated systems are photovoltaic, wind turbine and battery. The proposed elite converter is the high conversion ratio (HCR) DC–DC converter with a maximum power point tracking (MPPT) controller. The main advantage of the converter is to eliminate the problem of high switch voltage stress. The proposed hybrid technique is the joint execution of both the adaptive whale optimization algorithm (AWOA) and adaptive neuro-fuzzy interference system (ANFIS), and hence, it is named as AWO-ANFIS technique. Here, AWOA technique is utilized to establish the exact control signals based on the power variation between the source side (renewable energy sources and battery) and load side. ANFIS technique is utilized for error minimization by using the achieved dataset from AWOA technique. Here, the reference currents of the controller are managed in view of the MPPT for solar and wind energy. In the proposed technique, the objective function is defined by the system data subject to equality and inequality constraints. Finally, the proposed technique is executed in the MATLAB/Simulink working platform and the execution is compared with the existing techniques. To test the performance of the proposed method, different case studies are conducted such as variation of irradiance under constant wind speed, variation of PV power under constant irradiance and load variation under constant irradiance and wind speed. For all the cases, the response of current, voltage and power of all the sources is analyzed and compared with that of the existing techniques such as GA, PSO and WOA. The statistical analysis of the proposed technique is also analyzed. Likewise, the computation time using various trails such as 100, 250, 500 and 1000 is also analyzed. The comparison results affirm that the proposed technique has less computational time than the exiting techniques. Moreover, the proposed method is cost-effective power production of MG and effective utilization of renewable energy source without wasting the available energy.

An Adaptive Whale Optimization Algorithm Guided Smart City Big Data Feature Identification for Fair Resource Utilization

World improvement is the development of every single province of the world. Smart city implies changed hardware to adjusted individuals. Smart cities have the most indispensable part in altering distinctive regions of human life, touching segments like transportation, wellbeing, vitality, and instruction. Productively to make measurements to improve distinctive smart city benefits huge information frameworks are put away, prepared, and mined in smart cities. For the change and course of action of huge information applications for smart cities, different difficulties are faces. In this paper, we propose a wrapper display based ideal element recognizable proof calculation for ideal use of assets given highlight subset age. Nine component determination techniques used for compelling element extraction. At last, which includes best add to the ideal usage of assets got by means of a novel element recognizable proof calculation made by the application out of a Whale Optimization Algorithm with Adaptive Multi-Population (WOA-AMP) system as inquiry process in a wrapper display driven by the notable relapse demonstrate regression model Random Forest with Support Vector Machine (RF-SVM). Our proposed calculation gives the exact method to choose the most agreeable feature blend, which prompts ideal asset usage.

Study on sustainable development of power transmission system under ice disaster based on a new security early warning model

As the ecological climate deteriorates, common disasters including typhoons, earthquakes and ice disasters occur frequently in southern China. In particular, ice disasters can make severe ice coating on power line, resulting in power outage accidents. Not only do power supply disruptions cause major economic losses, but also they threaten people's lives in extremely cold conditions. Therefore, studying on the security early warning of ice coating damage to power line is significant and urgent. This paper adopts grey correlation analysis (GRA) to obtain 16 influence factors, of which the correlation degrees are over 0.74. In order to reduce the internal relevance among the impact factors, 5 common factors are extracted as the predictive model input values through factor analysis. With the input weight and hidden layer threshold optimized, a new extreme learning machine based on the adaptive whale optimization algorithm improved by chaotic sine cosine operator (CSCWOA-ELM) is established to forecast the ice coating damage to power line in southern China. To verify the accuracy and advancement of the proposed model, real data from the power repair projects under ice disaster are selected for experiments. The simulation results prove that the extraction of common factors can highly improve the prediction accuracy by approximately 27.80%. Compared with the 3 benchmark models, the CSCWOA-ELM model, of which the root mean square error (RMSE) is 0.02341 and the mean absolute percentage error (MAPE) is 1.82175%, shows a better performance in predicting the ice coating damage to power line.