Swarm Topology Research Articles

Extracting optimal fuel cell parameters using dynamic Fick’s Law algorithm with cooperative learning strategy and k-means clustering

This article introduces an enhanced stochastic search method tailored for optimizing the parameters of fuel cells (FCs), which hold significant relevance across various applications. The nonlinear nature of FCs poses a modeling challenge, prompting the proposal of an advanced Dynamic Fick’s Law Algorithm (DFLA). This improved approach incorporates a cooperative learning strategy, leveraging K-Means clustering, to derive optimal FC parameters. DFLA introduces a dynamic swarm topology by segmenting the population into subswarms, boosting diversity, and enabling broader global exploration. Simultaneously, the cooperative learning strategy fosters information exchange among subswarms, enhancing the algorithm’s ability to explore vast solution spaces and exploit diverse subswarm-derived solutions. The evaluation of DFLA utilized real-world datasets from commercial PEMFC stacks: 250-W stack, BCS 500-W, and NedStack PS6. Performance assessment relied on the Sum Squared Error (SSE), a standard evaluation metric, with comparisons drawn against established competing methods. The results underscored DFLA’s superior efficiency, showcasing improved performance metrics and convergence behaviors compared to the tested algorithms.

Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs

UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.

Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game

Autonomous cooperation of unmanned swarms is the research focus on "new combat forces" and "disruptive technologies" in military fields. The mechanism design is the fundamental way to realize autonomous cooperation. Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level, an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed. This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms, including the emergence of swarm intelligence, information network construction and collaborative mechanism design. Then, based on the framework, the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects: topology dynamics and strategy dynamics. Next, the unmanned swarms' community network is designed, and the network characteristics are analyzed. Moreover, the mechanism characteristics are analyzed by numerical simulation, focusing on the impact of key parameters, such as cost, benefit coefficient and adjustment rate on the level of swarm cooperation. Finally, the conclusion is made, which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.

PSO-Based Identification of the Li-Ion Battery Cell Parameters

The article describes the results of research aimed at identifying the parameters of the equivalent circuit of a lithium-ion battery cell, based on the results of HPPC (hybrid pulse power characterization) tests. The OCV (open circuit voltage) characteristic was determined, which was approximated using functions of various types, while making their comparison. The internal impedance of the cell was also identified in the form of a Thevenin RC circuit with one or two time constants. For this purpose, the HPPC pulse transients were approximated with a multi-exponential function. All of the mentioned approximations were carried out using an original method developed for this purpose, based on the PSO (particle swarm optimization) algorithm. As a result of the optimization experiments, the optimal configuration of the PSO algorithm was found. Three different cognition methods have been analyzed here: GB (global best), LB (local best), and FIPS (fully informed particle swarm). Three different swarm topologies were used: ring lattice, von Neumann, and FDR (fitness distance ratio). The choice of the cognition factor value was also analyzed, in order to provide a proper PSO convergence. The identified parameters of the cell model were used to build simulation models. Finally, the simulation results were compared with the results of the laboratory CDC (charge depleting cycle) test.

Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.

Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method. A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function. In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method. Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization

The Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A solution for achieving better user satisfaction would be to treat users individually and to offer educational materials in a customized way. Adaptive Curriculum Sequencing (ACS) plays an important role in education management system, for it helps finding the optimal sequence of a curriculum among various possible solutions, which is a typical NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel metaheuristic algorithm named Group-Theoretic Particle Swarm Optimization (GT-PSO) to tackle the ACS problem. GT-PSO would rebuild the search paradigm adaptively based on the solid mathematical foundation of symmetric group through encoding the solution candidates, decomposing the search space, guiding neighborhood movements, and updating the swarm topology. The objective function is the learning goal, with additional intrinsic and extrinsic information from those users. Experimental results show that GT-PSO has outperformed most other methods in real-life scenarios, and the insights provided by our proposed method further indicate the theoretical and practical value of an effective and robust education management system.

Group theoretic particle swarm optimization for gray-level medical image enhancement.

As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.

Cooperative Cellular UAV-to-Everything (C-U2X) communication based on 5G sidelink for UAV swarms

A swarm of cellular-connected Unmanned Aerial Vehicles (UAVs) enables new possibilities for emerging services and applications as the UAVs can autonomously coordinate their activities and cooperate to accomplish a given task. Because of spatio-temporal dynamics of swarm topology, robust and reliable network formation with seamless connectivity among UAVs is highly critical for any successful mission. This work focuses on the problem of cooperative and communication-aware UAV channel scheduling of data transmission from a set of target points of interests (PoIs) towards a cellular base station (BS). A novel, cooperative multi-hop communication model based on the C-V2X (Cellular Vehicle-to-Everything) Mode 4 cellular sidelink (PC5) radio interface is presented for efficient UAV data flow scheduling within the swarm. The model design aims at optimizing the cellular communications on UAV-to-UAV (U2U) and UAV-to-Infrastructure (U2I) links via a novel interference-aware scheduling, hence envisioning a new Cellular UAV-to-Everything (C-U2X) communication paradigm. An optimization model is used to solve the centralized version of the problem, while a distributed Dynamic Consensus-Based Bundle Algorithm (D-CBBA) is proposed to generate the best subchannel scheduling for maximizing data transmission in a distributed setting. Extensive simulation results demonstrate that the proposed distributed algorithm is able to extend the cellular infrastructure coverage while improving the multi-hop communications by autonomously adapting to the network conditions.

Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits

Real-time health assessments are of great importance for the safe and stable operation of in-orbit swarms. To solve the problems of existing real-time health assessments of microsatellite swarms, such as the difficulty of selecting a multisource and assessment calculation normalization, this paper proposes a real-time health assessment method applicable to mission-oriented swarms. The method divides the microsatellite swarm into three levels: single satellite, intersatellite communication link and swarm effectiveness, which establish a multilevel index system by adopting the reliability evaluation based on random failure and failure by loss, a health evaluation based on natural connectivity, and a real-time dynamic analysis based on swarm topology. For the swarm effectiveness during the mission, the multilevel index and the entropy weight method are used to construct the effectiveness evaluation model of the whole swarm, and the health state evaluation of the swarm is realized based on the variable weight principle. The simulation results show that this method can quantify the health state of the microsatellite swarm in real-time, and it can predict the health state after the fault without maintenance.

An automatic kriging machine learning method to calibrate meta-heuristic algorithms for solving optimization problems

For years, meta-heuristic algorithms have been widely studied and many improved versions have been developed: from the evolution of the swarm topologies of the Particle Swarm Optimization algorithm, to the using of machine learning to Differential Evolutionary algorithms. However, the tuning of the fundamental meta-heuristic parameters has been less studied, but may lead to significant improvements on the convergence accuracy of these algorithms. This paper aims at developing an automated methodology to calibrate the parameters of population-based meta-heuristic algorithms for optimization problems. Based on the kriging estimation of the best combination of parameters, the Automated parameter tuning of Meta-heuristics (AptM) methodology gives the optimal algorithm setup for each considered problem in order to lead to a better convergence accuracy. The proposed AptM methodology is used to tune three different meta-heuristic algorithms, each applied to twelve mathematical unimodal or multimodal objective functions. AptM methodology performance is assessed by comparison of classical setups usually used in the literature. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics with an average improvement of 62.02%, 69.12% and 64.94% on optimization problems defined in dimensions 10, 30 and 50 respectively. An experimental criterion is defined based on the convergence accuracy of the AptM methodology over the classical setups, assessing the AptM performances. The previous experimental criterion allows to compare the AptM methodology over the base-set. The AptM methodology shows a significant improvement of the algorithms performance on 97.2% of the tested problems.

Global Energy Consumption Optimization for UAV Swarm Topology Shaping

According to different mission scenarios, the UAV swarm needs to form specific topology shapes to achieve more robust system capability. The topology shaping, which will guide the UAVs autonomously to form the desired topology shape, is considered one of the most basic procedures in the UAV swarm field operations. The traditional optimization model of UAV swarm topology shaping proposed in most studies roughly represents the energy consumption by the squared Euclidean distances from initial positions to target positions of nodes. However, in practice, UAVs flying in different directions (vertical or horizontal) usually exhibits different energy consumption even though under the same moving distance. This paper proposes a more precise energy consumption model for UAV swarm topology shaping while taking the energy consumption for a UAV flying vertically upward, vertically downward, and horizontally into account. Simulation results show that the global energy consumption of the topology shaping modeled by the proposed energy consumption model is reduced by more than 38% on average compared with that using the traditional energy consumption model. Furthermore, to further reduce the global energy consumption, a translation vector is introduced in the optimization model to obtain the optimal topology shaping position of the UAV swarm system. Newton’s method is employed to derive the translation vector which exhibits good convergence. Simulation results show that the global energy consumption of optimal topology shaping position is reduced by 9.8% on average compared with that without translation.

A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity

Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94% in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.

Decentralised aerial swarm for adaptive and energy efficient transport of unknown loads

• We present a behaviour based subsumption architecture that enables a swarm’s topology to adapt to a load's mass distribution. • The decentralized self-organized swarm can perform a cooperative load transportation task without any explicit communication between members. • Our architecture has the potential to be scalable to a large number of agents. • This is significant in aerial swarm cooperative transport systems because it leads to even energy distribution among swarm members when compared to a uniform swarm topology. • It also fosters robustness of the load-suspended aerial robot system. Cooperative transport by a swarm of Quadcopters offers more flexibility and performance when carrying loads that are complex in structural profile and mass. Ensuring that team members of the swarm are optimally placed on these loads as well as able to resist disturbances from the environment during transport are current research challenges. In this paper, we present a decentralized behaviour based subsumption architecture for enabling a swarm of Quadcopters to explore an unfamiliar area, find a load and transport it to a target location cooperatively. In the architecture, three behaviours were used: an obstacle avoidance behaviour to avoid collisions with objects in the environment, a flocking behaviour to ensure swarm structure and a bacterium behaviour for exploration of the environment and to adapt to the mass profile of various detected loads. By adapting to the mass profile of a detected load, we show that our architecture ensures even energy distribution among Quadcopters while achieving robustness to disturbances from the environment. Our results show that a mass adapting swarm is able to conserve energy during payload transportation when compared to a swarm that does not adapt to a load’s profile. Furthermore, we do not use explicit communication between team members but instead rely on data from visual sensors attached to the Quadcopters. We experiment with simulations in a physics informed robot simulator called CoppeliaSim and demonstrate the effectiveness of our architecture when utilized for cooperative transport of irregular loads.

Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification.

As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.

Intelligent Decision Making and Bionic Movement Control of Self-Organized Swarm

In this article, we propose the intelligent decision making and bionic movement control of self-organized swarm. Swarm intelligent decision making can provide swarm path planning. Bionic movement control of self-organized swarm can simultaneously generate swarm formation, realize formation switching, and drive swarm while avoiding collision and preserving the existing communication topology. When the swarm topology switches, the swarm intelligent decision making is introduced to find out the optimal route between the initial position and destination for each intelligent unit. We propose an enhanced genetic algorithm (GA) to realize swarm intelligent decision making. Avoiding collision and preserving the existing communication topology should be achieved during the process of swarm moving. As a result, we design a new swarm motion potential function, which can realize bionic movement control. The stability of bionic movement control is proved to be effective in the collision avoidance, connectivity preservation, and formation generation. Simulation results and experiment results show that the enhanced GA and swarm motion potential function are effective.

Deep ${Q}$ -Learning-Based Node Positioning for Throughput-Optimal Communications in Dynamic UAV Swarm Network

In this paper, we study the communication-oriented unmanned air vehicle (UAV) placement issue in a typical manned-and-unmanned (MUM) airborne network. The MUM network consists of a few powerful aircraft nodes in the higher layer and high-density UAVs in the lower layer. While the aircraft network is relatively stable, the UAVs can form different swarm network topologies. Some UAVs are selected as gateway nodes to aggregate the received UAV data and send to a nearby aircraft which acts as a control node for the UAVs in a swarm. Assume a source UAV has data to be sent to its gateway node by using a route which may have broken links. Our goal is to guide the position of one or more relay UAVs to make up for the broken wireless links under the dynamic swarm topology. The placement of the relay node is determined by both traffic quality-of-service (QoS) requirements and the link conditions. We design a new queueing model, called multi-hop priority queue, to analyze the achievable QoS performance through multi-hop queue-to-queue accumulation modeling. To handle dynamic swarm topology and time-varying link conditions, we design a deep ${Q}$ -learning (DQN) model to determine the optimal link between two UAV nodes, and then use an optimization algorithm to locally fine-tune the position of the UAV node to optimize the overall network performance. The DQN-based UAV link selection is computed in the powerful aircraft (control node) which maintains the graphs of the swarm topology, where the optimization is implemented at the UAV. Our simulation results validate the throughput efficiency of our DQN-based UAV positioning scheme.

Resilience evaluation for UAV swarm performing joint reconnaissance mission.

The resilience of unmanned aerial vehicle (UAV) swarm is its joint capability to resist possible threat, adapt to disruptive events, and restore its intended performance under a specific time period. The quantitative assessment of the UAV swarm resilience requires a thorough understanding of its missions. In this paper, a mission-oriented framework is proposed to implement the resilience evaluation for the UAV swarm. Guided by the framework, the resilience evaluation for the UAV swarm performing joint reconnaissance mission is studied. A UAV swarm model is developed for joint reconnaissance mission based on complex networks and agent-based models. The following aspects of the UAV swarm are considered in the proposed model, namely, the mission orientation, UAV attributes, swarm topology, UAV cooperative strategy, UAV information exchange and fusion strategy, potential threats, recovery strategies, etc. Then, a novel performance metric is proposed to measure the mission capability of the UAV swarm performing joint reconnaissance mission. Results from the simulations show that, compared with existing studies, the proposed approach can provide more realistic and objective resilience evaluation for the mission-oriented UAV swarm. The above works can be used to support the decision making and the optimal design of the UAV swarm, given different missions.

Distributed Obstacles Avoidance For UAVs Formation Using Consensus-based Switching Topology

Nowadays, most of the recent researches are focusing on the use of multi-UAVs in both civil and military applications. Multiple robots can offer many advantages compared to a single one such as reliability, time decreasing and multiple simultaneous interventions. However, solving the formation control and obstacles avoidance problems is still a big challenge. This paper proposes a distributed strategy for UAVs formation control and obstacles avoidance using a consensus-based switching topology. This novel approach allows UAVs to keep the desired topology and switch it in the event of avoiding obstacles. A double loop control structure is designed using a backstepping controller for tracking of the reference path, while a Sliding Mode Controller (SMC) is adopted for formation control. Furthermore, collaborative obstacles avoidance is assured by switching the swarm topology. Numerical simulations show the efficiency of the proposed strategy.

Dynamic Swarm Attestation With Malicious Devices Identification

Nowadays, there is a rapid development of smart sensor network solutions. To prevent potential malicious attacks and make sure that the activities of devices are correct, there is a pressing need to ensure the software integrity of devices in the large-scale, dynamic, and self-organization swarm. Different from those single device attestation studies, Asokan et al. are the first to consider the secure swarm attestation problem and provide implementations based on two remote attestation architectures for embedded systems. However, their scheme could not provide a correct attestation result with dynamic swarm topology. Also, their scheme could not prevent a malicious device from launching a new attestation, and the adversary can use this weakness to conduct energy exhausting attack. In this paper, we mainly focus on secure and efficient identity attestation of compromised devices over swarm with dynamic topology and design an interactive swarm attestation scheme. Furthermore, we introduce multi-hop attestation security in swarm attestation and provide a verifiable solution for new attestation launching solution based on the one-way hash chain. Moreover, theoretical and practical analyses prove that our scheme is secure and efficient in attestation security and energy saving for dynamic swarm topology.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.