Hybrid Whale Research Articles

Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm

Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs and system performances. This research work examines the problems existing on cross-tier interfering as well as resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets), which involves macrocell with underlying femtocells”. Here, three primary contributions are concerned: a single objective issue together with power allocation, price allocation & subcarrier allocation of MC-FC networks. In addition, this research establishes a new hybrid algorithm termed Hybrid Whale and Rain Optimization (HW-RO) for solving the optimization problem in MC-FC Networks. Finally, the superiority of the suggested technique is evaluated in comparison to various existing approaches.

Vehicular pollution prediction using HWTO‐ANFIS model in urban areas of Hyderabad City

AbstractAir pollution has become a life‐threatening factor around the world in recent decades as a result of vehicles, industrialization, human activities, and urbanization. The existing vehicular air pollution prediction studies met several shortcomings in terms of poor prediction accuracy, higher error rates, huge execution time and etc. To tackle these challenges, this study proposed a Hybrid Whale Tabu Optimization‐based Adaptive Neuro‐Fuzzy Inference System (HWTO‐ANFIS) predict the effects of vehicular pollution in urban areas. The major objective of this paper is to minimize the forecasting error and get optimal results during pollution prediction. The average, minimum and maximum noise levels of vehicles are effectively predicted using the proposed HWTO‐ANFIS model. The proposed model efficiency is evaluated using various performance measures such as Normalized Mean Square Error, Mean absolute percent error, Mean Absolute Error, Average Error and Root Mean Square Error respectively. The MATLAB software performs the implementation work. Hyderabad city based vehicular pollution data is taken and analyzed to predict environmental pollution based on vehicles. For the analysis of the dataset, the number of pollutants is considered and the Air Quality Index (AQI) is also determined. The proposed hybrid method is tested and evaluated with the existing methods like Artificial Neural Networks (ANN) and Fuzzy Logic Controllers (FLC). In this study, the proposed HWTO‐ANFIS model is utilized to predict vehicular pollution in the last years such as 2018, 2019, 2020 and 2021, respectively. The proposed method's accuracy is 7%–12% greater than that of other conventional techniques. However, the proposed method offers superior detection performances than other state‐of‐art methods.

Energy management and operational planning of renewable energy resources-based microgrid with energy saving

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)” optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted.

A hybrid whale optimization algorithm based on equilibrium concept

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering.

Hybrid whale optimization algorithm based on symbiosis strategy for global optimization

Hybrid whale optimization algorithm based on symbiosis strategy for global optimization

GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification.

In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model's hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy.

Intelligent facial emotion recognition based on Hybrid whale optimization algorithm and sine cosine algorithm

The Whale Optimization Algorithm (WOA) is a new advanced algorithm that is based on the humpback whale chasing process. The main problem addressed by Whale Optimization Algorithm, similar to other metaheuristic algorithms, is premature convergence. This paper introduces the Sine Cosine Algorithm in the Whale Optimization Algorithm optimization process to enhance global convergence and to improve enactment. The proposed algorithm, called WOA-SCA, has local optima evasion of Whale Optimization Algorithm and high capability to increase the exploration. The implementation of the proposed WOA-SCA algorithm is investigated through a series of tests on common benchmark test functions, with the results compared to those of six alternative algorithms. The results of the investigations show that the proposed WOA-SCA algorithm works better in concert with the outputs of the benchmark functions. Also, to validate the proficiencies of the WOA-SCA, it has been used to resolve the real-world problem of Facial Emotion Recognition (FER). The field of facial emotion recognition is a fascinating new area of research that gives us the ability to identify the human face's expression in natural settings. A large part of the standard methodologies certainly does not indicate the looks because the attitudes depend on the human face sections' behaviors. The paper proposes the efficient Facial Emotion Recognition method using MultiSVNNN based on WOA-SCA (the proposed WOA-SCA based Multi-Support Vector Neural Network). Using the ADFES dataset for the disgust expression, our results demonstrate that the proposed work yields average emotion recognition accuracy of 98% associated to 80% and 96% based on BBPSO and MFOPSO, respectively. Investigational consequences indicate the virtuous enactment of the proposed scheme resolutions regarding precision.

An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

Two-dimensional Otsu multi-threshold image segmentation based on hybrid whale optimization algorithm

Two-dimensional Otsu multi-threshold image segmentation based on hybrid whale optimization algorithm

WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems

In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In order to solve these types of problems, metaheuristic algorithms are used. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. In this paper, the WOA algorithm is combined with the Sine Cosine Algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The WOA algorithm has superior performance in the exploration phase in contrast with the exploitation phase, whereas the SCA algorithm has weaknesses in the exploitation phase. The levy flight distribution has been used in the hybrid WOA and SCA algorithm to improve these deficiencies. This study introduced a novel hybrid algorithm named WOASCALF. In this algorithm, the search agents' position updates are based on a hybridization of the WOA, SCA, and levy flight. Each of these metaheuristic algorithms has reasonable performance, however, the Levy distribution caused small and large distance leaps in each phase of the algorithm. Thus, it is possible for the appropriate search agent to move in different directions of the search space. The performance of the WOASCALF has been evaluated by the 23 well-known benchmark functions and three real-world engineering problems. The result analysis demonstrates that the exploration ability of WOASCALF has strong superiority over other compared algorithms.

TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem

Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wolf optimization algorithm in conjunction with harmony search techniques. Test suite size was significantly reduced using the proposed approach, as shown by the analysis of the results. In order to assess the quality, speed, and scalability of TWGH, experiments were carried out on a set of well-known benchmarks. It was shown in tests that the proposed strategy has a good overall strong reputation test reduction size and could be used to improve performance. Compared with well-known optimization-based strategies, TWGH gives competitive results and supports high combinations (2 ≤ t ≤ 12).

HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

Optimized GAN for Text-to-Image Synthesis: Hybrid Whale Optimization Algorithm and Dragonfly Algorithm

Text to image synthesizes is a motivating and valuable task to explore in these days. The objective is to produce images from texts or captions, i.e. considering textual descriptions as input and deploying them to produce related images. For carrying this task, two challenging elements of image generation and language modeling should be combined that are said to be more complex than caption generation. This research aims to develop a new text-to-image synthesis method that includes the two crucial steps of (i) text to picture encoding and (ii) optimized GAN. At first, cross-modal feature grouping is done throughout the text to image encoding. Consequently, text embeddings are transformed into textual feature vectors using BI-LSTM. The images are created based on the encoding in the following stage. Thus, optimized GAN receives input from text feature groups and outputs the end synthesis images. Here, a research plans to train the GAN with optimization strategy via tuning the optimal weights using the Dragon Customized Whale Optimization (DC-WO) model. Finally, the superiority of the created approach is evaluated by comparing it to existing techniques. Additionally, the FID value of the proposed model using the cub dataset is 29.04%, 47.43%, 49.09%, 37.64% and 11.35%, superior to traditional FF, WOA, GWO, DA and GAN + CMAF models.

Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.

Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller

Water pumping system (WPS) plays a key role in human civilization, including agriculture, drinking, and industrial usage. To decrease the load demand on grid and provide electrical supply to WPS in rural areas, a locally placed standalone photovoltaic (PV)-based WPS is one of the best feasible solutions. Among many motors, brushless direct current motors (BLDCs) are attractive for WPS due to many advantages. However, integrating batteries to WPS will increase the cost and required maintenance, which will not be affordable for farmers. Hence, PV-fed BLDC motor-based WPS without battery is considered and implemented sensorless speed controller with sliding mode controller (SMC) in this article. Generally, power generated by PV systems will be affected by partial shading condition (PSC) and unable to produce maximum availability power due to failure of the conventional perturbed and observe (P&O) algorithm. In order to overcome this condition, a whale optimization algorithm (WOA) has been integrated to the conventional P&O algorithm in this article. A hybrid whale optimization–P&O (WOPO) algorithm can effectively work to extract the maximum possible power from PV during both PSC and normal condition. The results are compared with genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO) for maximum power point tracking (MPPT) under PSC. In order to reduce the cost of system, the inverter is also working as an MPPT device for the PV system by integrating the WOPO algorithm with SMC of inverter. Hence, an extra dc–dc converter will not be required for MPPT. Extensive results are presented with OPAL-RT to validate the proposed system under both steady-state and transient conditions. In addition to this, dSPACE-based hardware results are also presented in this article to validate the proposed system.

A Novel Hybrid Whale Optimization Algorithm for Flexible Job-Shop Scheduling Problem

The flexible job shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem and one of the more well-known NP-hard problems. To get better global optima of the FJSP, a novel hybrid whale optimization algorithm (HWOA) is proposed for solving FJSP, in which minimizing the makespan is considered as the objective. Firstly, the uniformity and extensiveness of the initial population distribution are increased with a good point set (GPS). Secondly, a new nonlinear convergence factor (NCF) is proposed for coordinating the weight of global and local search. Then, a new multi-neighborhood structure (MNS) is proposed, within which a total of three new neighborhoods are used to search for the optimal solution from different directions. Finally, a population diversity reception mechanism (DRM), which ensures to some extent that the population diversity is preserved with iteration, is presented. Seven international benchmark functions are used to test the performance of HWOA, and the results show that HWOA is more efficient. Finally, the HWOA is applied to 73 FJSP and four Ra international instances of different scales and flexibility, and the results further verify the effectiveness and superiority of the HWOA.

A Hybrid Whale Genetic Algorithm for Feature Selection in Biomedical Dataset

One of the major concerns of biomedical datasets is high dimensionality. These dimensions may include irrelevant and redundant features that adversely affect the performance of classification algorithms. Extensive research has been done in the area of machine learning to handle high dimensionality. In literature, feature selection algorithms have been developed for this purpose. In this paper, a hybrid nature-inspired algorithm is proposed which is a combination of whale optimization algorithm and genetic algorithm for feature selection. The proposed algorithm is applied to four microarray datasets and one DNA sequence dataset and compared with classical feature selection methods. In all the algorithms decision tree classifier is mainly employed. To reach an approximate best solution and remove local solutions, the exploitation and exploration phases are balanced efficiently in the proposed algorithm. The convergence speed in the proposed algorithm is accelerated by the adaptive mechanisms. Overall results employ better performance on the majority of datasets.

(Retracted) Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation performance of the proposed HWOAL method has been compared with other algorithms, such as WOA, SSO, SCA, BAT, and other two recent segmentation algorithms, such as WOA-TH and BWO on several benchmark images (BSD 300). The experimental result is carried in 30 trials and analyzed based on objective fitness value, optimal multilevel threshold values, segmentation quality measures, such as mean square error, peak signal-to-noise ratio, structural similarity index, average difference, and computation time to compute optimal multilevel threshold values. The experiment conducted by the authors shows that the proposed HWOAL method is efficient, produces better fitness value, and segmentation metrics for multilevel image segmentation than other algorithms.

Hybrid Optimization Algorithm for VM Migration in Cloud Computing

The number of cloud users and their respective workload increases everyday with the inherent benefits of cloud computing. On the other hand, it becomes critical for service providers to maintain Quality of Service (QoS) even under heavy workload conditions. In order to provide better computing services, cloud utilizes Virtual Machine (VM) migration techniques, which eases the process of providing the services to the user without any delay and with minimum energy consumption. The existing cloud computing services mainly rely on migration techniques; nevertheless, handling large VM migrations consumes more energy, which directly affects the VM performances. This necessitates the need to develop an effective VM migration strategy to perform the necessary migrations and avoid unnecessary migrations. Conventional migration techniques perform migration based on static parameters which attains less efficient results and lags in performance while handling the resource utilization. In this research work, a hybrid optimization algorithm is presented to handle VM migration in a cloud environment. Cuckoo search optimization algorithm and particle swarm optimization algorithm are combined to obtain the proposed hybrid optimization model. The major objective of this research work is to reduce energy consumption, computation time, and migration cost. Maximizing resource utilization is another objective of this research work. To validate the research objective, the performance of hybrid optimization model is verified through simulation analysis and compared with conventional algorithms like firefly optimization, whale optimization, hybrid whale optimization, and hybrid bee colony optimization in terms of energy consumption, migration cost, resource availability, and computation time.

Survivable virtual network embedding algorithm considering multiple node failure in IIoT environment

The Industrial Internet of Things is a new ecology formed by the deep integration of the Internet of Things and the industrial environment. Network is one of the core technologies of industrial Internet of things. However, in the face of billions of industrial Internet of Things applications, the traditional network architecture cannot meet the needs of these applications for network resources. Network virtualization technology can effectively solve the above problems by decoupling network services from the underlying network. However, the existing heuristic virtual network embedding algorithm is easy to fall into the local optimum, such as the virtual network embedding algorithm based on particle swarm optimization. Therefore, in order to solve the above problems, a virtual network embedding strategy based on the hybrid whale optimization algorithm is proposed. This strategy can avoid the introduction of the population into the local optimum while reducing the amount of calculation. Secondly, a node importance measurement strategy based on weighted aggregation coefficient and traffic prediction is introduced. This strategy can conduct a more reasonable and effective assessment of the importance of nodes. Thirdly, to reduce resource occupation and improve network security and stability, we propose a multi-node failure recovery strategy. It depends on the construction of candidate nodes and the selection of recovery points. Simulation results show that compared with other algorithms, the proposed algorithm reduces the cost and maintains a high success rate of fault recovery. In addition, its request acceptance rate is twice that of other algorithms.