A Four-Phase Improvement of Grover＇s Algorithm

When applying Grover＇s algorithm to an unordered database, the probabifity of obtaining correct results usually decreases as the quantity of target increases. A four-phase improvement of Grover＇s algorithm is proposed to fix the deficiency, and the unitary and the phase-matching condition are also proposed. With this improved scheme, when the proportion of target is over 1/3, the probability of obtaining correct results is greater than 97.82% with only one iteration using two phases. When the computational complexity is O（ √M/N）, the algorithm can succeed with a probability no less than 99.63%.

An improvement of the fast uncovering community algorithm

Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. （Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Star. Mech. 10 10008） is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree （OV） that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness.

Improvement of Stochastic Competitive Learning for Social Network

As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

An Improved Iterated Greedy Algorithm for Solving Rescue Robot Path Planning Problem with Limited Survival Time

Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

FPGA-based plasma sterilization device for wound-edge recognition

There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic steriliza-tion.For this reason,in this work,a plasma sterilization device with wound-edge recognition was developed using afield-programmable gate array(FPGA)and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds.SOLIDWORKS was used to design the mechanical structure of the device,and it was manufactured using 3D printing.The device used an improvement of the traditional Sobel detection algorithm,which extends the detection of edges in only the x and y directions to eight directions(0○,45○,90○,135○,180○,225○,270○,and 315○),completing the wound-edge detection by adaptive thresholding.The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional move-ment;the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction.In this way,motors are used to move the plasma jet and achieve automatic,efficient,and accurate plasma sterilization.It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level.The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and aflow rate of 0.6 slm,while at the biological-tissue level,these values were 1 mm/s and 0.6 slm,respectively.

Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

An improved RRT∗algorithm,referred to as the AGP-RRT∗algorithm,is proposed to address the problems of poor directionality,long generated paths,and slow convergence speed in multi-axis robotic arm path planning.First,an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency.Second,a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm.Finally,the planning path is processed by pruning,removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm.Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform,the results show that the AGP-RRT∗algorithm reduces 87.34%in terms of the average running time and 40.39%in terms of the average path cost;Meanwhile,under two sets of complex environments A and B,the average running time of the AGP-RRT∗algorithm is shortened by 94.56%vs.95.37%,and the average path cost is reduced by 55.28%vs.47.82%,which proves the effectiveness of the AGP-RRT∗algorithm in improving the efficiency of multi-axis robotic arm path planning.

Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.

ATwo-LayerOptimal Scheduling Strategy forRural Microgrids Accounting for Flexible Loads

In the context of China’s“double carbon”goals and rural revitalization strategy,the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids.Considering the operational characteristics of rural microgrids and their impact on users,this paper establishes a two-layer scheduling model incorporating flexible loads.The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid,while the lower-layer aims to minimize the total electricity cost for rural users.An Improved Adaptive Genetic Algorithm(IAGA)is proposed to solve the model.Results show that the two-layer scheduling model with flexible loads can effectively smooth load fluctuations,enhance microgrid stability,increase clean energy consumption,and balance microgrid operating costs with user benefits.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

A Novel Bi-Level VSC-DC Transmission Expansion PlanningMethod of VSC-DC for Power System Flexibility and Stability Enhancement

Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability enhancement.First,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and stability.Subsequently,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production simulation.Finally,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning schemes.The results verified that the proposed method can achieve excellent RE curtailment with high flexibility and stability.Furthermore,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

考虑交货期的双资源柔性作业车间节能调度

为解决含有机器和工人双资源约束的柔性作业车间节能调度问题,在考虑交货期的基础上,建立了以总提前和拖期惩罚值及总能耗最小为目标的双资源柔性作业车间节能调度模型。提出了一种改进的非支配排序遗传算法(improved non-dominated sorting genetic algorithmⅡ,INSGA-Ⅱ)进行求解。针对所优化的目标,设计了一种三阶段解码方法以获得高质量的可行解;利用动态自适应交叉和变异算子以获得更多优良个体;改进拥挤距离以获得收敛性和分布性更优的种群。将INSGA-Ⅱ与多种多目标优化算法进行对比分析,实验结果表明所提算法可行且有效。

Improved parallel weighted bit-flipping algorithm

An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck（QC-LDPC） codes.