Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

SUNDER:Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

Effect of group size and regrouping on physiological stress and behavior of dairy calves

Mixing or regrouping of calves from different pens is a common animal management practice on the farm, which frequently occurs after weaning and has a negative effect on calve welfare. Social integration before regrouping may relieve stresses, but more evidences are needed to verify this hypothesis. The present study aimed to investigate acute physiological and behavioral variations of individually-or group-housed calves after being introduced into a mixed group. A total of 132 postnatal calves were randomly divided into groups of 1, 3, 6 and 12 animals(S, G3, G6, and G12;6 replicates in each group) until 59 days of age. At 60 days of age, every two replicates from different groups(S, G3, G6 and G12)were introduced in a larger pen which containing 44 of the aboved experimental calves. Before and after regrouping,physiological parameters of stress, including heart rate(HR), saliva cortisol(S-CORT), saliva secretory immunoglobulin A(SIgA), interleukin-2(IL-2), interleukin-6(IL-6), tumor necrosis factor-α(TNF-α) levels, and behavioral responses were recorded. After regrouping, HR and S-CORT increased immediately(P<0.05), and higher(P<0.05) levels of such molecules were found in S calves compared to those in group-housed calves. Levels of SIgA and IL-2 were decreased(P<0.05), and the lowest(P<0.05) IL-2 values were found in S calves compared to those in group-housed calves. In addition, the introduced calves displayed a distinct behavior, including altered active and rest time, which was associated with negative emotions triggered by the novel surroundings. Allogrooming, play, exploration behaviors and lying time were increased significantly(P<0.05) in group-housed calves than those in S calves. Conversely, self-grooming, aggressive behaviors, standing and walking time were increased(P<0.05) in S calves than those in group-housed calves. These findings suggest that individually-housed calves may be more susceptible to stressors arising from regrouping than grouphoused calves, which consequently negatively affected behavioral and neuroendocrine responses. Furthermore, moving calves with previous social experience may help mitigate regrouping stress.

UAV Frequency-based Crowdsensing Using Grouping Multi-agent Deep Reinforcement Learning

Mobile CrowdSensing(MCS)is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles(UAVs)as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes rescue.In this paper,we focus on scheduling UAVs to sense the task Point-of-Interests(PoIs)with different frequency coverage requirements.To accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach(G-MADDPG)to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm(DCA),then it regards each team as an agent.In this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.

Grouping response method for equivalent static wind loads based on a modified LRC method

Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by structural designers. A method for equivalent static wind loads applicable to multi-responses is proposed in this paper. A modified load- response-correlation （LRC） method corresponding to a particular peak response is presented, and the similarity algorithm implemented for the group response is described. The main idea of the algorithm is that two responses can be put into one group if the value of one response is close to that of the other response, when the structure is subjected to equivalent static wind loads aiming at the other response. Based on the modified LRC, the grouping response method is put forward to construct equivalent static wind loading. This technique can simultaneously reproduce peak responses for some grouped responses. To verify its computational accuracy, the method is applied to an actual large-span roof structure. Calculation results show that when the similarity of responses in the same group is high, equivalent static wind loads with high accuracy and reasonable magnitude of equivalent static wind distribution can be achieved.

Relationship between CT grouping and complications of liver cirrhosis

BACKGROUND: Imaging examination is important for hepatic cirrhosis. But the relationship between magnetic resonance (MR), computed tomography (CT) or ultrasound findings and pathological groups, degree, or reserve function of the cirrhotic liver is not clear. In this study, we investigated the relationship between the CT groupings of liver cirrhosis and its complications and clinical conditions. METHODS: The CT findings in 357 patients with liver cirrhosis were grouped. The complications were analyzed, included splenomegaly, varicose collateral veins, ascites, pleurorrhea, primary liver carcinoma, gallbladder stone, etc. Blood routine (BRt), and serum usea nitrogen (SUN), creatinine and uric acid were measured and hypersplenia and liver-kidney syndrome were estimated. RESULTS: Three hundred and fifty-seven patients with cirrhosis were divided into homogeneous group (87 patients, 24.4%), segmental group (41, 11.5%), and nodal group (229, 64.2%). The grade of spleen enlargement in the segmental and the nodal groups was significantly greater than that in the homogeneous group (P <0. 01 and P<0.001). The patients with varices were shown in a descending order in the segmental group (70.7%), the nodal group (17.0%) and the homogeneous group (2.3%), respectively. Significant difference was observed among the 3 groups ( P < 0.001). Ascites was seen in 182 patients (79.5%) of the nodal group, in 11 patients (26.8%) of the segmental group and in 9 patients ( 10.3%) of the homogeneous group (P<0.001). Sixty-eight patients (29.7%) in the nodal group had primary liver carcinoma and 1 (2.4%) in the segmental group and 5 (5.8%) in the homogeneous group (P<0.001). The number of patients with decreased concentration of hematoglobin in the nodal group was more than that in the homogeneous group ( P < 0. 001). The mean values of hematoglobin and platelet in the nodal group and the segmental group were significantly lower than those in the homogeneous group ( P < 0. 05 ). The number of patients with increased concentration of SUN in the nodal group and the segmental group was more than that in the homogeneous group (P<0.005). The concentration of SUN in the nodal group was significantly higher than that in the homogeneous group (P <0.002). CONCLUSION: There is a close relationship between the grouping of liver cirrhosis by CT findings and complications caused by the cirrhosis. The grouping is significant for estimating clinical conditions.

Energy-Efficient Resource Allocation for Heterogeneous Network with Grouping D2D

In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.

An Adaptive Grouping Scheme in Ultra-Dense IEEE 802.11ax Network Using Buffer State Report Based Two-Stage Mechanism

IEEE 802.11ax,which is an emerging WLAN standard,aims at providing highly efficient communication in ultra-dense wireless networks.However,due to a large number of stations(STAs)in the ultra-dense device deployment scenarios,the potentially high packet collision rate significantly decreases the communication efficiency of WLAN.In this paper,we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report(BSR)based Two-stage Mechanism(BTM).In order to achieve the optimal efficiency of BSR delivery,we analyze the functional relationship between STA number in group and Resource Unit(RU)efficiency.Based on this analysis results,an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM.The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

Molecular Grouping of Grateloupia Tissues Collected Along Chinese Coast and Microsatellite Diversity Analysis of G. asiatica

Genus Grateloupia is one of the most speciose genera in family Halymeniales. It is also one of the sources for natural materials, food and medicine. With different environments, Grateloupia change their morphological characteristics, making their morphological identification very difficult. In addition, few of the species diversity in this genus has been described before. In this study, phylogenetic analysis based on rbc L gene sequence was employed to group Grateloupia collected from three locations along Chinese coast. The microsatellites were also used to evaluate their genetic diversity. In total, the tissue parts of 6 putative species were collected from G. asiatica, G. livida, G. lanceolate, G. catenata, G. turuturu and G. filicina. In order to evaluate their genetic diversity and then conserve them better, 40 microsatellites available for Grateloupia were used to evaluate their genetic diversity, and 11 microsatellites were found to be applicable to determine the genetic diversity of G. asiatica. It was found that the genetic diversity of G. asiatica around Qingdao was very rich. We suggested that the species of genus Grateloupia should be identified based on rbc L phylogenetic analysis before the diversity evaluation with microsatellites. The microsatellites should be developed for each species of Grateloupia so that their genetic diversity can be evaluated appropriately.

The grouping characteristics of sea waves in the Shijiu Port

A review concerning the methods of studying and describing wave groups is presented in this paper. After analysing 78 field records collected in the Shijiu Port, China, the measured parameters of wave groups and some factors describing wave groupness and their variations are given. Moreover, these results are compared with those of theory.

AI-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research attention.In the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and virtual.However,they can be exposed to threats.Particularly,various hacker threats exist.For example,users’assets are exposed through notices and mail alerts regularly sent to users by operators.In the future,hacker threats will increase mainly due to naturally anonymous texts.Therefore,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term memory.Additionally,several application versions are used.Currently,research on tasks and performance for algorithm application is underway.We propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the metaverse.The algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing grouping.Consequently,we create 24 topic-based models.Assuming normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.

A Privacy-Preserving Grouping Proof Protocol Based on ECC with Untraceability for RFID

An RFID (Radio-Frequency IDentification) system provides the mechanism to identify tags to readers and then to execute specific RFID-enabled applications. In those applications, secure protocols using lightweight cryptography need to be developed and the privacy of tags must be ensured. In 2010, Batina et al. proposed a privacy-preserving grouping proof protocol for RFID based on ECC (Elliptic Curve Cryptography) in public-key cryptosystem. In the next year, Lv et al. had shown that Batina et al.’s protocol was insecure against the tracking attack such that the privacy of tags did not be preserved properly. Then they proposed a revised protocol based on Batina et al.’s work. Their revised protocol was claimed to have all security properties and resisted tracking attack. But in this paper, we prove that Lv et al.’s protocol cannot work properly. Then we propose a new version protocol with some nonce to satisfy the functions of Batina et al.’s privacy-preserving grouping proof protocol. Further we try the tracing attack made by Lv et al. on our protocol and prove our protocol can resist this attack to recover the untraceability.

Grouping occurrences being the fundamental feature of the strong earthquakes in Chinese mainland

The temporal and spatial distributions of all the 259 M≥6 earthquakes of eastern Chinese mainland (λ≥108° E) and its adjacent area and all the 153 M≥7 earthquakes of western Chinese mainland (λ<108°E) and its adjacent area up to 1991 were analyzed systematically.These earthquakes are called as strong earthquakes and those of the east before and after 1600,and those of the west before and after 1900, are called respectively as former ones and latter ones.Most of these events were divided into 45 sets of which the each relatively concentrated in both time and space and took the form of group.In the grouping form,the probability of occurrence of a strong earthquake higher than the average appeared simultaneously in time and space.The grouping occurrences of the strong earthquakes of Chinese mainland has been determined as a fundamental feature of the seismicity.

利用Grouping替代法构造强度为3的非对称正交表

Suen等提出了关于水平是2的幂次的强度为3的正交表的替代法.本文将这种替代法进行推广,针对水平是3的幂次的情况,提出一种新的Grouping替代法构造非对称正交表.

Ability Grouping——Learners' and Teachers' Perception at Chinese Universities

This study showed that difference in students’perceptions toward ability grouping existed.Even though teachers and scholars expected that ability grouping could help students to become more positive motivated and satisfied with studying in the class thus narrowing the gap between advanced and beginning level students,there were still problems showing that ability grouping should be modified.