Dynamic Grouping Strategy Research Articles

An Improved BSA with Dynamic Grouping Strategy and Its Application in UAV Path Planning

An Improved BSA with Dynamic Grouping Strategy and Its Application in UAV Path Planning

DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection

Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods to detect fraud from these intricate graphs. However, those methods neglect two distinct characteristics of the fraud graph: the non-additivity of certain attributes and the distinguishability of grouped messages from neighbor nodes. This paper introduces the Dynamic Grouping Aggregation Graph Neural Network (DGA-GNN) for fraud detection, which addresses these two characteristics by dynamically grouping attribute value ranges and neighbor nodes. In DGA-GNN, we initially propose the decision tree binning encoding to transform non-additive node attributes into bin vectors. This approach aligns well with the GNN’s aggregation operation and avoids nonsensical feature generation. Furthermore, we devise a feedback dynamic grouping strategy to classify graph nodes into two distinct groups and then employ a hierarchical aggregation. This method extracts more discriminative features for fraud detection tasks. Extensive experiments on five datasets suggest that our proposed method achieves a 3% ~ 16% improvement over existing SOTA methods. Code is available at https://github.com/AtwoodDuan/DGA-GNN.

A Dynamic Grouping Strategy for Beyond Diagonal Reconfigurable Intelligent Surfaces With Hybrid Transmitting and Reflecting Mode

A Dynamic Grouping Strategy for Beyond Diagonal Reconfigurable Intelligent Surfaces With Hybrid Transmitting and Reflecting Mode

Aquila optimizer with dynamic group strategy for global optimization tasks

SummaryThe Aquila optimizer (AO) is an efficient method for solving global optimization problems. However, the evolution of each individual learns from experience in the same group, which can easily fall into local optima. Therefore, this paper adopts the dynamic grouping strategy (DGS) of the population and proposes an improved AO algorithm to solve the global optimization problem. Different from the original AO algorithm, the DGSAO algorithm only evolves the individuals with the worst fitness in each group each time, which increases the diversity of the population. In order to verify the effectiveness of the algorithm, we tested it on 23 benchmark functions, among which the dimensions of to are 30, 100, and 200 dimensions. The experimental results show that the DGSAO algorithm is an effective method for solving global optimization problems. At the same time, we also conduct experiments on two engineering design problems, and the results show that the DGSAO algorithm can obtain a competitive result.

Bi-level intelligent dynamic path planning for an UAV in low-altitude complex urban environment

An offline and online bi-level structure-based dynamic path planning algorithm is proposed for an unmanned aerial vehicle (UAV) in low-altitude complex urban environment. First, an improved Hunger Games Search (HGS) algorithm is developed to generate an offline optimized path under the UAV’s performance constraints and the known static obstacles’ constraints. The individuals of the proposed algorithm will be divided into multiple groups to increase the population diversity. And then, a dynamic grouping strategy and a quantum-behaved behavior are proposed to solve the premature convergence’s problem and the imbalance problem between exploration and exploitation ability in HGS. To improve the dynamic obstacle avoidance efficiency of the algorithm, the dynamic obstacles are classified into three categories: newly added no-fly zone, known and unknown dynamic obstacles. Then, utilizing the information of the offline optimized path and the airborne sensors, three kinds of online planning strategies—an improved rapid-exploring random tree (RRT), a changing speed strategy, and a novel three-dimensional rolling windows—are introduced to dynamically update the path or speed of the UAV. Simulation results indicated that the improved HGS can enhance the performances of the traditional HGS and outperform other compared algorithms on the benchmark functions. Meanwhile, the online planning strategies can effectively achieve dynamic obstacle avoidance within the constraints of offline path. More specially, the planning time and angles of the local path to avoid the no-fly-zone’s influence are improved by 11.3% and 56.8% through utilizing the improved RRT.

Fish Migration Optimization with Dynamic Grouping Strategy for Solving Job-Shop Scheduling Problem

<p>Aiming at the job-shop scheduling problem (JSP), a dynamic grouping fish migration optimization (DFMO) is proposed to solve it. The DFMO algorithm adopts a multi-group structure to improve the convergence ability of the algorithm. And the opposition-based learning (OBL) strategy is applied to the group with poor overall fitness value to improve its solving environment. This paper proposes three different communication strategies to exchange information between different groups. In order to better determine the communication time between groups, a dynamic detection method based on population diversity is proposed. Compared with the static method of determining the communication time between groups, the proposed method can make the group more fully explore the current area and more hopefully find the optimal solution. The experiment in this paper is divided into two parts, one part is the numerical experiment test, the other part is the JSP problem standard library test. From the experimental results, the DFMO algorithm can obtain good results in both parts of the experiment, and has a good problem optimization ability.</p> <p> </p>

Dynamic Cooperative Coevolution for Large Scale Optimization

The cooperative coevolution (CC) framework achieves a promising performance in solving large scale global optimization problems. The framework encounters difficulties on nonseparable problems, where variables interact with each other. Using the static grouping methods, variables will be theoretically grouped into one big subcomponent, whereas the random grouping strategy endures low efficiency. In this paper, a dynamic CC framework is proposed to tackle the challenge. The proposed framework works in a computationally efficient manner, in which the computational resources are allocated to a series of elitist subcomponents consisting of superior variables. First, a novel estimation method is proposed to evaluate the contribution of variables using the historical information of the best overall fitness. Based on the contribution and the interaction information, a dynamic grouping strategy is conducted to construct the dynamic subcomponent that evolves in the next evolutionary period. The constructed subcomponents are different from each other, and therefore the required parameters to control the optimization of each subcomponent vary a lot in each evolutionary period. A stage-by-stage parameter adaptation strategy is proposed to adapt the optimizer to the dynamic optimization environment. Experimental results indicate that the proposed framework achieves competitive results compared with the state-of-the-art CC frameworks.

Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration

Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization, and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE Network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

Impacts of a dynamic grouping strategy on students' learning effectiveness and experience value in an item bank‐based collaborative practice system

AbstractPast research has pointed out that a good grouping strategy can improve the learning effectiveness of collaborative learning. However, the grouping strategy adopted in most past research is static grouping. This results in some disadvantages. Therefore, this study developed an item bank‐based collaborative practice system and proposed a dynamic grouping method which uses the concepts of weighted quizzes and bit coding as the rule of a regrouping algorithm. The method and the traditional static method were applied to two classes of sophomore students of a university of science and technology in the central region of Taiwan. There were 76 valid samples which included 38 students of the experimental group and 38 of the control group. The control group adopted the static grouping method, and the experimental group adopted the dynamic grouping method. The experimental duration was 9 weeks of 3 hours per week. Experimental results showed that the collaborative experience value and the learning effectiveness of the experimental group were significantly greater than those of the control group. This indicates that the experimental group students made more efforts to conduct coordination, discussion and finally reach consensus with their partners, and made greater progress than the control group students.

위험분산을 위한 다층적 그룹핑 전략 : 문재인 정부 신남방 정책 구상의 전략적 배경과 이행방안

위험분산을 위한 다층적 그룹핑 전략 : 문재인 정부 신남방 정책 구상의 전략적 배경과 이행방안

A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence, in order to further improve the search capability of the solution algorithm, a cooperative co-evolution (CC) algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm (MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy

Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance (SCA) problem has attracted more and more attention. Taking into consideration the large-scale flight planning in a global view, SCA can be formulated as a large-scale combinatorial optimisation problem with complex constraints and tight couplings between variables, which is difficult to solve. In this paper, an SCA approach based on the cooperative coevolution algorithm combined with a new decomposition strategy is proposed to prevent the premature convergence and improve the search capability. The flights are divided into several groups using the new grouping strategy, referred to as the dynamic grouping strategy, which takes full advantage of the prior knowledge of the problem to better deal with the tight couplings among flights through maximising the chance of putting flights with conflicts in the same group, compared with existing grouping strategies. Then, a tuned genetic algorithm (GA) is applied to different groups simultaneously to resolve conflicts. Finally, the high-quality solutions are obtained through cooperation between different groups based on cooperative coevolution. Simulation results using real flight data from the China air route network and daily flight plans demonstrate that the proposed algorithm can reduce the number of conflicts and the average delay effectively, outperforming existing approaches including GAs, the memetic algorithm, and the cooperative coevolution algorithms with different well-known grouping strategies.

Conflict Resolution Based on Cooperative Coevolutionary with Dynamic Grouping Strategy for Multi-Aircraft

Conflict resolution problem (CRP) plays a crucial role in the guarantee of safety. This paper formulates CRP as a multi-agent path planning problem which aims to find optimal paths for aircrafts. An algorithm named CCDG is proposed to tackle it based on cooperative coevolutionary (CC) with a dynamic grouping strategy for aircraft. CCDG makes aircraft divided into several equal sub-groups according to the dynamic grouping strategy. Each sub-group can adopt an evolutionary algorithm (EA) to optimize the aircrafts paths fully distributed and in parallel. Optimal solution is obtained through cooperation and coordination with all sub-populations. Empirical studies using extremely scenario adopted by previous research show that CCDG outperformed the existing approach (the fast GA), and the popular path planner that each aircraft uses an EA. Moreover, CCDG succeed to improve the airspace safety and reduce cost for CR.

A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary

Conflict resolution (CR) plays a crucial role in safe air traffic management (ATM). In this paper, we propose a new hybrid distributed-centralized tactical CR approach based on cooperative co-evolutionary named the CCDG (cooperative co-evolutionary with dynamic grouping) strategy to overcome the drawbacks of the current two types of approaches, the totally centralized approach and distributed approach. Firstly, aircraft are divided into several sub-groups based on their interdependence. Besides, a dynamic grouping strategy is proposed to group the aircraft to better deal with the tight coupling among them. The sub-groups are adjusted dynamically as new conflicts appear after each iteration. Secondly, a fast genetic algorithm (GA) is used by each sub-group to optimize the paths of the aircraft simultaneously. Thirdly, the aircraft’s optimal paths are obtained through cooperation among different sub-groups based on cooperative co-evolutionary (CC). An experimental study on two illustrative scenarios is conducted to compare the CCDG method with some other existing approaches and it is shown that CCDG which can get the optimal solution effectively and efficiently in near real time, outperforms most of the existing approaches including Stratway, the fast GA, a general evolutionary path planner, as well as three well-known cooperative co-evolution algorithms.